In Figure , performance of the di↵erent cross-validation strategies is better depicted: the values of AUC and BEDROC (20) for each schema, compound representation and algorithm are compared between them and with a random prediction. Error bars show the standard deviations obtained from subsampled estimates. It can be seen that random strategy outperforms the rest in all the possibilities. After random, clustering-based strategy had the best performance both in terms of AUC and BEDROC (20). Database-based and intermediate strategies have both poor performance, specially in terms of BEDROC (20). The same behavior can be seen in ROC curves of all possibilities in Figure of the Supporting (20) metrics between pairs of models. Di↵erences are calculated subtracting column performances from rows. Crossed tiles indicate that difference for that pair of models is not statistically significant (↵ = 0.05).