Furthermore, once we generated 100-200 top diverse hit scaffolds, as illustrated in Figure , we employed ROCS (Rapid Overlay of Chemical Structures) [45] and other ligand-based machine learning models to search for compounds in the archive or/and prioritize designed compounds synthesis based on shape, electrostatic properties, pharmacophoric features, and other 2D or 3D characteristics of the compounds and protein pocket residues. ROCS is a powerful virtual screening tool known for its ability to rapidly identify putatively active compounds through the shape comparison. It has demonstrated strong competitiveness and often outperforms structure-based approaches in the virtual screening . Notably, ROCS has been instrumental in identifying novel and interesting molecular scaffolds, particularly for targets that were traditionally challenging for computational techniques . In our case study, we used individual 3D SDF files of the top 100-200 diverse hit scaffolds as the input for ROCS searching against a pre-compiled Novartis archived library Omega database with a Tanimoto Combo score cutoff of 1.0. For all the molecules identified from the ROCS results, we calculated the GBVI/WSA ΔG score as the predicted binding affinity for each compound-protein pair using the same MOE method described in detail as above. We applied the same GBVI/WSA ΔG score cutoff as used in the SAR enrichment step. The compounds that passed the GBVI/WSA ΔG score filter underwent further filtering based on 1) available quantity to ensure sufficient amounts for biological testing; 2) LC-QC (Liquid Chromatography-Quantitative Control) filter to ensure matched analytical evaluation of the compounds; 3) Novartis global solubility machine learning prediction and solubility experimental filter to ensure the compounds' solubility for biological testing. Moreover, the filtered compounds eligible for other ligand-based machine learning models can be seamlessly integrated into this workflow for orthogonal screening compound set selection, such as the profile-quantitative structure-activity relationship (pQSAR) models from Novartis or customized models specific to the target's mechanism of action. Ultimately, the selected compound set is subjected to biological activity testing.