The GBT-Small model was built as an ensemble of five separate GBT models, referred to as ensemble mates. The training data was split into five subsets based on chemical clusters using the SciKit Learn 48 "Stratified GroupKFold" function. Clusters were determined using "LeaderPicker" from the SimDivPickers module of RDKit 2023 . Each mate was trained only on 4 of the 5 folds. This helped to ensure each mate was seeing a diderent distribution of chemical space. Each of the mates is trained independently of the others, and each mate is asked to predict the probability that a compound is active. These predictions are then averaged to generate the final predicted probability of being active with the standard deviation among predictions acting as an "ensemble entropy like" confidence assessment . GBT-Small used only binary FCFP6 fingerprints implemented in RDKit.