The Python package and notebooks that accompany this work have been carefully designed to allow complete reproducibility of the annotation and variant landscape analysis. However, their primary purpose is to empower readers to self-assess variant effects on protein bioactivity. As shown here for the clinically relevant kinase EGFR, among other data-rich targets, these analyses can identify clusters of chemical space with varying effects on bioactivity, specific protein structural traits causing differing bioactivity patterns, and compounds with desirable selectivity profiles. These results not only are in line with the literature and enabled the analysis of activating and resistance-inducing substitutions, but also extended beyond the most widely-recognized variants and chemical classes . In turn, they can be used as hypothesis generators in drug design as well as recommendation systems to include or remove certain chemical clusters or variants from a prospective modelling or virtual screening task . Indeed, for a target like EGFR with a high variant bioactivity percentage and differential bioactivity profiles across variants and chemical groups, our bioactivity modelling results indicated a decrease in predictive performance when variants were not accounted for, generalizing the effects previously observed when modelling cyclooxygenases 1 and 2 . Both removing variant data from the QSAR model and explicitly modelling each variant in a PCM model increased performance in random split crossvalidation, likely by reducing the negative effect of noise . Similar results were observed for other proteins with a high variant bioactivity percentage despite large inter-target variability.