Each one of the cross-validation strategies is analyzed in terms of imbalance and data redundancy to better understand and interpret performance results. First, active/inactive proportion is explored for each cross-validation schema. Then, overlap of targets and compounds between split sets is computed as a percentage with respect to the total number of targets (88) and compounds (32,950), respectively. Lastly, distribution of chemotypes and protein classes is explored for each strategy. For targets, this distribution is studied for the main protein families. Since for molecules there is no such classification, we decided to group them in terms of their Bemis-Murcko sca↵old (BMS), a technique for extracting molecular frameworks by removing side chain atoms which has been used for clustering compounds. Performance metrics