In this work, we introduced a novel open-source consensus docking workflow. We presented our comprehensive evaluation of DockM8, using datasets such as DEKOIS 2.0, DUD-E, and Lit-PCBA. We demonstrated its competitive performance in VS over existing methods, including state-of-the-art machine learning and advanced consensus approaches. The versatility and adaptability of DockM8, characterized by its ability to handle a variety of docking algorithms and scoring functions, enable it to achieve high levels of generalizability and performance across diverse targets. This is a notable advancement over existing tools, which often show limited applicability across different targets. Furthermore, DockM8 addresses critical challenges in the field, particularly the accessibility and usability of VS tools in academic research. Its user-friendly interface, minimal programming requirements, and opensource nature democratize the use of advanced VS methods. This approach not only makes cutting-edge computational drug discovery tools more accessible to a wider range of scientists but also challenges the prevailing notion that cost is indicative of quality in software solutions. Despite its current strengths, there remain opportunities for further enhancement of the DockM8 workflow. Areas such as the speed of the procedure, integration of advanced machine-learning docking and activelearning algorithms, and improvements to the GUI are identified as key directions for future development. The potential inclusion of pre-filtering steps and the integration of novel docking score prediction methodologies could further augment the efficacy and efficiency of DockM8 in handling large compound libraries.