This study is focused on the characterization of the data imbalance present in bioactivity datasets, as well as how to address it. Bioactivity data also poses the problem of chemical series, i.e. sets of similar molecules with similar activities, that result in inflated performance metrics when split between training and test sets. We addressed those via a clustering prior to the splitting, ensuring that similar molecules would belong to the same set.