Recently, I introduced a novel docking approach that integrates the strengths of established programs, such as AutoDock Vina, with high-accuracy quantum mechanical (QM) data . Previously referred to as QFVina or QFVinardo-based on the Vina and Vinardo scoring functions-this approach will now be more generally called QFD (QuantumFuture's Docking scheme). QFD decouples the tasks of conformational search and strain-energy calculation from the docking process itself. This design addresses one of the primary weaknesses of traditional docking software by focusing the scoring process on intermolecular interactions and desolvation energies, while employing precomputed QM conformations that include accurate ab initio DFT-based strain energies (obtained with basis sets such as def2-TZVP and modern functionals like rev-SCAN ). Our studies have demonstrated that accurately capturing conformational relative energies and strain energies requires a detailed, high-level physics-based description of intramolecular interactions. Even the best semiempirical QM approaches and high-level neural network methods like the ANI family cannot match ab initio DFT for robustness and quality. On the other hand, all scoring functions in practical docking workflows, whether empirical, physics-based, or AI/ML-based-must be orders of magnitude faster than even semiempirical QM or deep neural network computations. Consequently, a common shortcoming among these scoring functions is their inaccuracy in evaluating conformational relative energies and strain energies, often leading to excessive numbers of false positives and an inability to effectively identify truly potent inhibitors within a reasonable hit-list size. By reducing reliance on both inaccurate conformational searches and unreliable strain-energy estimations, QFD significantly enhances docking accuracy and lowers the rate of false positives in hit lists.