In the last decade, there has been an important increase in the amount of open bioactivity data in public data repositories. Databases with biological activity annotations have been extensively reviewed. As case in point, the latest release of the ChEMBL database (v31) contains data for 14,855 targets, 2,786,911 distinct compounds, and 19,286,751 activities. With the abundance of publicly available bioactivity data, it becomes imperative to extract, curate and explore information of interest for drug discovery, so data-driven drug discovery models have emerged. Analyzing information derived from large-scale structure-Activity relationships (SAR) data can contribute to understanding the underlying mechanism associated with the structural transformations of compounds that modify their activity, in the prediction of potential target proteins for therapeutics and in network pharmacology, linking targets in terms of shared active ligands. Similarly, available bioactivity data plays an important role in computational chemogenomics to develop predictive models . Other approaches focus on identifying and describing biologically relevant regions of chemical space to guide the design and synthesis of new compounds. One of the reasons is that experimental structural data are still sparse compared to compound activity and physicochemical data. The current protocols' limitations only partially enable the generation of reliable 3D conformational states or binding modes. On the other hand, recent progress in AI-driven de novo structure prediction (see Section 4.3) has provided an unprecedented wealth of putatively reliable structural templates, with coverage recently approaching the entire protein universe.