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diff --git a/resources/libraries/python/MLRsearch/goal_result.py b/resources/libraries/python/MLRsearch/goal_result.py
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+++ b/resources/libraries/python/MLRsearch/goal_result.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 GoalResult class."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+from .discrete_load import DiscreteLoad
+from .relevant_bounds import RelevantBounds
+from .trimmed_stat import TrimmedStat
+
+
+@dataclass
+class GoalResult:
+    """Composite to be mapped for each search goal at the end of the search.
+
+    The values are stored as trimmed stats,
+    the conditional throughput is returned as a discrete loads.
+    Thus, users interested only in float values have to convert explicitly.
+
+    Irregular goal results are supported as instances with a bound missing.
+    """
+
+    relevant_lower_bound: Optional[TrimmedStat]
+    """The relevant lower bound for the search goal."""
+    relevant_upper_bound: Optional[TrimmedStat]
+    """The relevant lower upper for the search goal."""
+
+    @staticmethod
+    def from_bounds(bounds: RelevantBounds) -> GoalResult:
+        """Factory, so that the call site can be shorter.
+
+        :param bounds: The relevant bounds as found in measurement database.
+        :type bounds: RelevantBounds
+        :returns: Newly created instance based on the bounds.
+        :rtype: GoalResult
+        """
+        return GoalResult(
+            relevant_lower_bound=bounds.clo,
+            relevant_upper_bound=bounds.chi,
+        )
+
+    @property
+    def conditional_throughput(self) -> Optional[DiscreteLoad]:
+        """Compute conditional throughput from the relevant lower bound.
+
+        If the relevant lower bound is missing, None is returned.
+
+        The conditional throughput has the same semantics as load,
+        so if load is unidirectional and user wants bidirectional
+        throughput, the manager has to compensate.
+
+        :return: Conditional throughput at the relevant lower bound.
+        :rtype: Optional[DiscreteLoad]
+        """
+        if not (rlb := self.relevant_lower_bound):
+            return None
+        stat = next(iter(rlb.target_to_stat.values()))
+        return rlb * (1.0 - stat.pessimistic_loss_ratio)