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diff --git a/resources/libraries/python/MLRsearch/selector.py b/resources/libraries/python/MLRsearch/selector.py
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+# Copyright (c) 2023 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Module defining Selector class."""
+
+
+from dataclasses import dataclass, field
+from typing import Callable, List, Optional, Tuple
+
+from .dataclass import secondary_field
+from .discrete_load import DiscreteLoad
+from .discrete_width import DiscreteWidth
+from .expander import TargetedExpander
+from .global_width import GlobalWidth
+from .limit_handler import LimitHandler
+from .measurement_database import MeasurementDatabase
+from .relevant_bounds import RelevantBounds
+from .target_spec import TargetSpec
+from .strategy import StrategyBase, STRATEGY_CLASSES
+
+
+@dataclass
+class Selector:
+    """A selector is an abstraction that focuses on only one of search goals.
+
+    While lower-level logic is hidden in strategy classes,
+    the code in this class is responsible for initializing strategies
+    and shifting targets towards the final target.
+
+    While the public methods have the same names and meaning as the ones
+    in strategy classes, their signature is different.
+    Selector adds the current target trial duration to the output of nominate(),
+    and adds the current bounds to the input of won().
+
+    The nominate method does not return a complete Candidate instance,
+    as we need to avoid circular dependencies
+    (candidate will refer to selector).
+    """
+
+    final_target: TargetSpec
+    """The target this selector is trying to ultimately achieve."""
+    global_width: GlobalWidth
+    """Reference to the global width tracking instance."""
+    initial_lower_load: DiscreteLoad
+    """Smaller of the two loads distinguished at instance creation.
+    During operation, this field is reused to store preceding target bound."""
+    initial_upper_load: DiscreteLoad
+    """Larger of the two loads distinguished at instance creation.
+    During operation, this field is reused to store preceding target bound."""
+    database: MeasurementDatabase = field(repr=False)
+    """Reference to the common database used by all selectors."""
+    handler: LimitHandler = field(repr=False)
+    """Reference to the class used to avoid too narrow intervals."""
+    debug: Callable[[str], None] = field(repr=False)
+    """Injectable function for debug logging."""
+    # Primary above, derived below.
+    current_target: TargetSpec = secondary_field()
+    """The target the selector is focusing on currently."""
+    target_stack: List[TargetSpec] = secondary_field()
+    """Stack of targets. When current target is achieved, next is popped."""
+    strategies: Tuple[StrategyBase] = secondary_field()
+    """Instances implementing particular selection strategies."""
+    current_strategy: Optional[StrategyBase] = secondary_field()
+    """Reference to strategy used for last nomination, needed for won()."""
+    # Cache.
+    bounds: RelevantBounds = secondary_field()
+    """New relevant bounds for this round of candidate selection."""
+
+    def __post_init__(self) -> None:
+        """Initialize derived values."""
+        self.target_stack = [self.final_target]
+        while preceding_target := self.target_stack[-1].preceding:
+            self.target_stack.append(preceding_target)
+        self.current_target = self.target_stack.pop()
+        self._recreate_strategies()
+
+    def _recreate_strategies(self) -> None:
+        """Recreate strategies after current target has changed.
+
+        Width expander is recreated as target width is now smaller.
+        For convenience, strategies get injectable debug
+        which prints also the current target.
+        """
+        expander = TargetedExpander(
+            target=self.current_target,
+            global_width=self.global_width,
+            initial_lower_load=self.initial_lower_load,
+            initial_upper_load=self.initial_upper_load,
+            handler=self.handler,
+            debug=self.debug,
+        )
+
+        def wrapped_debug(text: str) -> None:
+            """Call self debug with current target info prepended.
+
+            :param text: Message to log at debug level.
+            :type text: str
+            """
+            self.debug(f"Target {self.current_target}: {text}")
+
+        self.strategies = tuple(
+            cls(
+                target=self.current_target,
+                expander=expander,
+                initial_lower_load=self.initial_lower_load,
+                initial_upper_load=self.initial_upper_load,
+                handler=self.handler,
+                debug=wrapped_debug,
+            )
+            for cls in STRATEGY_CLASSES
+        )
+        self.current_strategy = None
+        self.debug(f"Created strategies for: {self.current_target}")
+
+    def _update_bounds(self) -> None:
+        """Before each iteration, call this to update bounds cache."""
+        self.bounds = self.database.get_relevant_bounds(self.current_target)
+
+    def nominate(
+        self,
+    ) -> Tuple[Optional[DiscreteLoad], float, Optional[DiscreteWidth]]:
+        """Find first strategy that wants to nominate, return trial inputs.
+
+        Returned load is None if no strategy wants to nominate.
+
+        Current target is shifted when (now preceding) target is reached.
+        As each strategy never becomes done before at least one
+        bound relevant to the current target becomes available,
+        it is never needed to revert to the preceding target after the shift.
+
+        As the initial trials had inputs relevant to all initial targets,
+        the only way for this not to nominate a load
+        is when the final target is reached (including hitting min or max load).
+        The case of hitting min load raises, so search fails early.
+
+        :returns: Nominated load, duration, and global width to set if winning.
+        :rtype: Tuple[Optional[DiscreteLoad], float, Optional[DiscreteWidth]]
+        :raises RuntimeError: If internal inconsistency is detected,
+            or if min load becomes an upper bound.
+        """
+        self._update_bounds()
+        self.current_strategy = None
+        while 1:
+            for strategy in self.strategies:
+                load, width = strategy.nominate(self.bounds)
+                if load:
+                    self.current_strategy = strategy
+                    return load, self.current_target.trial_duration, width
+            if not self.bounds.clo and not self.bounds.chi:
+                raise RuntimeError("Internal error: no clo nor chi.")
+            if not self.target_stack:
+                if not self.bounds.clo and self.current_target.fail_fast:
+                    raise RuntimeError(f"No lower bound: {self.bounds.chi!r}")
+                self.debug(f"Goal {self.current_target} reached: {self.bounds}")
+                return None, self.current_target.trial_duration, None
+            # Everything is ready for next target in the chain.
+            self.current_target = self.target_stack.pop()
+            # Debug logs look better if we forget bounds are TrimmedStat.
+            # Abuse rounding (if not None) to convert to pure DiscreteLoad.
+            clo, chi = self.bounds.clo, self.bounds.chi
+            self.initial_lower_load = clo.rounded_down() if clo else clo
+            self.initial_upper_load = chi.rounded_down() if chi else chi
+            self._update_bounds()
+            self._recreate_strategies()
+
+    def won(self, load: DiscreteLoad) -> None:
+        """Update any private info when candidate became a winner.
+
+        :param load: The load previously nominated by current strategy.
+        :type load: DiscreteLoad
+        """
+        self._update_bounds()
+        self.current_strategy.won(bounds=self.bounds, load=load)