The main motivation for this work will be demonstrated in the next section by analyzing and extending an existing database for studying conformational relative energies and rankings of druglike molecules using widely adopted methods. It will be shown that the challenges in predicting conformational relative energies are often underestimated by the docking community. Current force fields, semi-empirical QM methods, and advanced AI-based methods, such as ANI family of methods, exhibit deficiencies in accuracy compared to state-of-the-art ab initio methods. The scoring function in docking, on the other hand, needs to be significantly faster than even the fastest force fields analyzed in the next section. Thus, relying on such scoring functions to identify physically realistic conformational geometries and to estimate rankings and relative energies of conformations may be irrational, regardless of whether the scoring function is empirical, physicsbased, or machine learning-driven. I will provide details on our current QM-based conformational libraries, designed for drug repurposing research, in the following section. Practical development aspects of QFVina and QFVinardo docking will be presented afterward followed by discussions about computational costs. Results will then be compared to the traditional flexible ligand Vina and Vinardo docking outcomes by analyzing strain energies and docking hit lists, using three targets from COVID-19 research as examples. Finally, conclusions and potential next steps for further development will be discussed in the last section.