The genetic variability landscape of ChEMBL, the most widely used public bioactivity database in computational drug discovery, was comprehensively analysed for the first time. Key advantages resulting from years of expert knowledge gathering in ChEMBL's variant annotation pipeline were identified through parallel annotation. Additionally, misannotations requiring future correction were found. Recommendations for pipeline enhancement were provided, alongside a proposal for simplified annotation of target variants for bioactivity modelling, which are made available in a modelling dataset. The amount and distribution of variant data across protein organisms, families, individual proteins, and variants were extensively described. Furthermore, a Python package and notebooks were developed to assess variant effects on bioactivity distributions and modelling performance. The potential of these analysis tools to extract variants and promising chemical candidates was demonstrated, particularly for data-rich proteins. Particularly, informed decisions for noise reduction in bioactivity models and modelling variant bioactivity can be facilitated using our approach. activity label (Supplementary Figure ). The data_validity_comment field was used to drop low-quality data (author confirmed error), as done in the Papyrus dataset. The activity_comment field was also used to define active and inactive binary labels when pchembl_value was not available.