The general trends highlighted above were consistent across the data-rich proteins, although few of them had a significant performance improvement when using variant-aware models (Table , Figure ). On a protein-specific level, this effect can be traced back to data sparsity and imbalance across variants and subsets of the chemical space (Figure and Supplementary Figure for EGFR and HIV-1 RNaseH-RT, respectively). In fact, tackling these issues by reducing the applicability domain with a similarity-expanded common subset resulted in equivalent or improved PCM performance in random split cross-validation compared to complete sets for these proteins, with a clear advantage over the variant-agnostic model (Supplementary Table , Figure ). Moreover, the analysis of the bioactivity patterns can help explain discrepancies from the general modelling trend. For example, among data-rich proteins, BRD4 (O60885) displayed the biggest increase in performance when using variant-aware models in random split cross-validation (Figure ). Following the general trend, we expected a good extrapolation to novel variants for this protein, which was not the case (Figure ). The examination of the substituted residue distance to the ligand's centroid on the bioactivity cluster map for BRD4 (Supplementary Figure ) highlighted that the two most represented variants, Y97A and Y390A respectively, are each part of different protein domains, bromodomains 1 and 2 respectively, corresponding to different binding sites, and had therefore opposite effects on bioactivity for the subset of compounds examined. This was confirmed in the protein's structure and explained the lack of generalization power of the model, which might be improved by splitting the chemical space into domain-specific binders.