The AutoDockVina (ADV) program written in Python vs3.8 included into the PyRx-098/PyRx-1.0 package (), and home-modified to handle large number of ligands, was used as described before (Supplementary Materials / ADV.mp4). As recently reported, ADV was the best binding predictor for 428 protein-ligand complexes (± 2 Å), among 9 other docking programs, including 2 new amino acid sequence-smile-only algorithms, with the highest 52.3 % of success prediction rate . ADV was employed here to explore alternative docking cavities than those identified by DW, to compare ADV affinities with DW docking-scores, to estimate relative docking in ~nanoMolar (nM) affinities and to generate detailed PyMol protein / children 3D images 42, 46-48 . Briefly, Obabel minimization and *.pdbqt file conversion of F13L proteins and children ligands were generated by employing the mmff94s (Merck) force-field (most similar to the DWBEL mmff94s+). Children ligands were supplied to Obabel-ADV as DWBEL generated *sdf files, saved as mentioned above to preserve their 2D geometries. Only one highest affinity conformer per child were analyzed in this work. Estimates of ADV ~ docking-scores in Kcal/mol , were converted to ~ nM affinities by the formula, 10 9 *(exp (Kcal/mol/0.592) ). A blind-docking grid of 45x45x45 Å centered by PyMol / centerofmass, surrounding the whole F13L molecule models was employed.