PM6/COSMO score normalization (e.g. by the number of heavy atoms, molecular weight, etc.) to minimize the size-related bias of the empirical scoring functions toward larger molecules being ranked at the top of a score-ranked list of molecules. is conducted under the hood during ML model training. However, PM6/COSMO could be individually improved by applying the "pROC-Chemotype" protocol , which could help to overcome some chemistryrelated bias in SQM scoring. The DEKOIS2.0 database is ideal for this purpose, as it contains a small but diverse number of bioactives and virtual decoys that covers all known chemical space per target. That protocol can help to intuitively explore the underlying reasons for variations in SQM scoring performance, e.g. changes in protonation/tautomerization states, molecular flexibility, and other parameters characteristic of some chemotype clusters. By adjusting parameters in the PM6/COSMO or building a database of chemotypes/protein binding site characteristics and respective PM6/COSMO corrections, the Eint term, which is the driving force of our proposed scoring scheme, could be improved, and would in turn leverage the robustness of the SQM-ML SF.