A splitting strategy based on compound clustering (both of actives and inactives) was applied to the bioactivity data, omitting target information. Clustering-based validation strategies have been used to avoid the compound series bias, making sure that there are no similar molecules both in training, validation and test sets. We followed the implementation of our previous study on cross-validation strategies in PCM, where K-means clustering with k = 100 was applied to the fingerprint description of the compounds. Data was divided in training, validation and test sets with a proportion of 80/10/10%. This splitting was randomly performed 10 times (folds) in order to test the consistency of the results, thus training and testing each model in 10 different data partitions. As further explained in the next subsection, for some balancing strategies the clustering was applied before the resampling and for others it was applied afterwards.