Bioactivity prediction is one of the key techniques in the computational drug discovery pipeline, mostly applied in virtual screening campaigns . Quantitative structure-activity relationship (QSAR) modelling has been around for a long time and can be used to predict ligand bioactivity for a target of interest based on the compound's chemical structural characteristics . Over time other bioactivity prediction strategies have emerged that include information other than chemistry-derived features . An example is proteochemometric (PCM) modelling, where the protein characteristics are considered in addition to ligand molecular structure, allowing for bioactivity predictions on several targets simultaneously .