where RBFE and EXP are relative binding free energy prediction and experimental measures, respectively. This heuristic has been previously described by our group and has been used to generate ML models for post-hoc correction of free energy predictions. Optimal design principles suggest that networks containing many edges with low magnitude |∆∆G offset | values will yield ∆G estimates more accurate with respect to experimental binding affinities than networks containing many edges with high magnitude |∆∆G offset | values. However |∆∆G offset | is not a practical metric to select an RBFE network a priori since it requires knowledge of the experimental measure, and the prediction from the chosen RBFE protocol.