In the pursuit of accelerating drug discovery, computational methods have become indispensable, with structure-based virtual screening (SBVS) emerging as a cornerstone strategy. Docking-based VS uses 3D structural information and computational algorithms to sift through large molecular libraries, identifying compounds with potential biological affinity for a specified target. Consequently, this screening reduces the number of compounds subjected to "wet-lab" assays, substantially curbing associated costs while hoping to discover novel active compounds . Molecular docking simulates the interaction between a ligand and its target, attempting to predict the binding pose of the ligand of interest and computing a docking score used to rank potential ligands based on predicted binding affinities . Determining the accurate binding pose of a small molecule is essential not only for assessing its binding affinity but also for leveraging the pose in lead optimization. Furthermore, accurate DockM8: An All-in-One Open-Source Platform for Consensus Virtual Screening in Drug Design A PREPRINT scoring of compounds is crucial for comparing ligands in a screening scenario and determining whether a particular ligand should be considered for experimental validation. Conventional molecular docking software, including AutoDock , AutoDock Vina , and Glide , predominantly employ heuristic search algorithms to systematically examine a range of potential ligand conformations. Although the sampling of binding poses performed by these algorithms has seen significant advances in recent years, particularly with recent developments, challenges in scoring and ranking the resulting poses still remain due to known shortcomings such as not considering protein flexibility, water-mediated interactions, or changes in entropy upon binding. Over the past decades, a multitude of scoring functions (SFs) have been developed, ranging from empirical, physics-based, and knowledge-based SFs to the more recently developed machine-learning (ML) based SFs . Despite significant recent advances in this field, these scoring functions still lack universal applicability as they necessarily rely on a simplification of the complexity of protein-ligand binding. Indeed, the performance of any given scoring function on a given target does not necessarily correlate to performance on other targets . Moreover, single scoring functions often struggle to handle both accurate binding affinity prediction and ligand ranking . Several solutions have been proposed to deal with the issues surrounding the performance of molecular docking, including consensus docking (using multiple methods) and ensemble docking (incorporating several protein conformations). Ensemble docking uses multiple receptor conformations to tackle the inherent challenge of protein flexibility. Indeed, early SBVS campaigns are mostly performed using flexible ligands and rigid receptor docking. Although this accelerates the calculation, it fails to represent the dynamics of the protein-ligand system under study. Ensemble docking suggests that using multiple protein states instead of a single static structure offers a more holistic view of ligand binding. This approach improves the predictive accuracy of molecular docking by considering scores and rankings across these various protein states . Consensus docking has also emerged as a modality seeking to enhance the precision of molecular docking. By combining diverse docking algorithms and scoring functions, consensus approaches circumvent individual limitations through aggregation. This strategy was shown to yield more reliable binding pose predictions, better absolute and relative scoring of ligands, and superior generalizability . In recent years, a variety of consensus docking tools have been developed (Table ). DockBox uses a variety of docking algorithms (AutoDock4 , Vina and DOCK ) along with score-based consensus docking to enhance pose prediction and ligand ranking . dockECR uses LeDock , rDock , Smina and Vina along with exponential consensus ranking (ECR) to improve binding pose prediction . Approaches such as MetaDOCK , CompScore or ESSENCE-Dock attempt to find novel methods of carrying out the consensus by using docking-pose based clustering, incorporating additional SF components in the consensus, or using pose RMSD and the number of rotatable bonds to further refine the consensus scoring. Also recently, DockingPie offers a graphical user interface (GUI) in PyMOL to aid in consensus screening. x -x 1 0 x DockBox x x -2 5 -dockECR x x -1 4 -DockStream x x -0 >5 pyscreener x x -0 >5