For training the bioactivity predictor, we selected a subset of 240 SMILES from the ExCAPE database such that they are representative of only two topological scaffolds. This was done to mimic usual real-world scenarios where existing experimental datasets are limited in size and diversity, especially in the early phases of drug discovery projects, and the aim is to explore the chemical space to identify novel molecules. ECFP6 vectors of length 2048 were generated using RDKit. Binary activity labels were obtained from a DRD2 oracle model described in Appendix D, resulting in 62 active samples and 178 inactive ones. After performing a 5-fold crossvalidation, we fit a Scikit-learn Random Forest Classifier (RFC) with 200 estimators and a maximum tree depth of 10 to all 240 samples. Model performance was measured across different classification metrics described in Appendix A and reported in Additional file 1: Table .