Clustering. Structurally diverse groups of molecules in the training set were identified using average distance agglomerative clustering, implemented with scikit-learn v. 1.2.1. A distance matrix of all molecules was used based on the 'Tanimoto distance' (computed as 1 -Tanimoto similarity) on ECFPs. The hierarchical clustering dendrogram was cut at a Tanimoto distance (T d ) of T d = 0.8 to find moderately diverse subclusters and at T d = 0.61, T d = 0.70, and T d = 0.70 for PKM2, VDR, and ALDH1 respectively to find smaller, low diversity, sub-subclusters. These specific cut-off values were chosen so that each dataset contained 10 clusters. All subclusters had a minimal size of 128 molecules and contained a single sub-subcluster of 64 molecules. Additionally, clusters should contain a minimal number of hits, comparable to the proportion of hits in the full dataset, as determined by: