DockM8 continued: While DockM8 demonstrates excellent performance, several areas for improvement remain. Enhancing speed could be achieved by transitioning from multiple .sdf files to a database system , and incorporating novel machine-learning docking methods like Karmadock or UMol to expedite the time-intensive docking step. Integrating sophisticated scoring functions and pre-filtering techniques, such as pharmacophore models or fingerprint similarity, could further refine performance. Additionally, adopting recent docking score prediction and active-learning approaches like HASTEN and DeepDocking could facilitate rapid virtual screening of extensive chemical libraries. Improvements to the graphical user interface (GUI) are also planned to make data visualization and interaction more intuitive. We encourage the computational chemistry community to contribute to DockM8's development via our GitHub repository.