We identified this tendency to be largely due to use of inverse-frequency weighting , which scales the impact of the ligands on the model based on the inverse probability of their reference free energy in the training set (SI Fig. ). While this technique was intended to compensate for bias due to increasing number of active compounds in the greedy and narrowing selections, it also has a side-effect causing ligands from poorly sampled regions of the ∆G spectrum to have a larger impact on the loss function. Inverse-frequency weighting does not significantly change the free energy distribution of sulfonyl containing ligands. Instead it makes other ligands less likely to be classified as weak binders. Therefore, while compensating for bias due to the free energy distribution of the training set, inverse-frequency weighting also exposes bias due to ligand composition.