The third category of quality includes force field-based methods, with the GFN-FF method being an exception, showing performance close to the semi-empirical QM level. This analysis highlights that widely used force fields generally lack the accuracy needed to estimate conformational relative energies and ranks. Even semi-empirical QM methods and the most advanced AI-based models, which have computational costs similar to QM semi-empirical methods, fall short in accurately predicting conformational strain energy and ranking. The physics-based, empirical, or AI-based scoring functions used in current docking programs are even less accurate than the force fields mentioned above, as they require lower computational costs. Due to this inaccuracy in conformational strain energies, docking hit lists are often filled with false positives. Even if docking results identify favorable conformations with strong protein interactions, the scoring functions cannot accurately determine whether the conformation has zero or 5 kcal/mol strain energy, or anything in between. Consequently, hit lists must be larger than desired to ensure true positives are identified, as the number of false positives is large and increases with the size of the docking project.