To examine the effect of structural heterogeneity in the starting set, we artificially created subsets of molecules with different degrees of structural diversity. Diversity was defined through molecular similarity by computing the Tanimoto coefficient on Extended Connectivity Fingerprints 41 (ECFPs), which captures the presence of shared substructures. Using hierarchical clustering, we selected ten clusters in each dataset with moderate structural diversity (average Tanimoto similarity ranging from 0.26 ± 0.03 to 0.28 ± 0.03, Table ). Within each of these clusters, another subcluster of molecules with low diversity was identified (average similarity higher than 0.36 ± 0.01, Table ). This gave us a hierarchy of three levels of structural diversity (Fig. ): (1) 'innate' diversity, representing the inherent diversity of the full screening library, (2) moderate diversity, and (3) low diversity. When constructing the starting set, molecules could be sampled from areas of each level accordingly. Since our approach is hierarchical, this allowed us to vary molecular diversity in the starting set while staying in the same population of molecules for each of the ten experimental replicates. Fingerprints between all molecules in the starting set.