In this paper, we propose a hybrid Semiempirical Quantum Mechanical (SQM) -Machine Learning (ML) protein-ligand (P-L) scoring function, dubbed SQM-ML, as a docking pose re-scoring tool to tackle the structure-based drug design problem. We demonstrate that explicit computation of deformation energies and ligand conformational entropies imposes a great computational burden without any apparent performance benefit. Instead, we leverage ML to reinforce the raw PM6-D3H4X/COSMO interaction energy with specially selected descriptors that are related to the ligand entropy. To compensate for inaccuracies of the PM6-D3H4X/COSMO SQM method, we also reinforce it with P-L interaction fingerprints projected to low dimensional space. We showcase the classification power of SQM-ML on 17 diverse ligand decoy sets and its superiority over the baselines, namely PM6-D3H4X/COSMO interaction energy and Glide SP, and explain its weaker performance in some data sets through ML model interpretation. To deal with the bottleneck of our protocol, which is the SQM optimization and scoring, we suggest an active learning approach, in which an ulta-fast ML model is trained to predict SQM-ML scores from 2D molecular fingerprints and could be employed in VS campaigns. Despite the unquestioned superiority of SQM-ML in this study, we need to emphasize that we don't guarantee similar performance in unseen data with physicochemical property values and chemotypes not encountered in our training data. Our future direction is to expand the training set with more diverse compounds and improve SQM-ML's generalizability. All the code machinery to produce the hybrid SQM/MM P-L scoring function is made available for practitioners at .