To judge chemical diversity, we used Taylor-Butina clustering at various Tanimoto distance thresholds (0.2, 0.3, and 0.4). Taylor-Butina takes a distance threshold as input that forces compounds within the same cluster to be within that threshold. Table summarizes the number of compounds, number of clusters, and number of unique clusters that are in each cross-validation fold. The number of unique clusters that are in a given fold but not the others can measure a virtual screening model's ability to generalize. Because we generated cross-validation folds by splitting on library and activity instead of cluster membership, the clusters are not uniformly distributed across folds.