A range of different feature sets was used to identify efficient encodings for describing ∆G of f set . A general trend in feature set performance can be observed across ML models. MolProps and combinatorial feature sets (fingerprints appended to MolProps) fit the training set better than standalone fingerprints (APFP, TOPOL and ECFP6), and X-NOISE performs worst as expected since this feature set is generated from random data.