The first observation was that clustering modified data imbalance in a strategy-dependent way. When the starting set was perfectly balanced (strategy resampling_before_clustering), clustering and splitting induced a degree of imbalance, particularly visible in the heavier tails of the active ratios distributions in the test set. Compared to training, the lower sample sizes in the test set may also cause extreme imbalances more often. On the other end, this effect was only moderate in no_resampling, where the distribution of actives ratio was similar in train and test, but that of test had more extreme proteins with either all actives or all inactives.