One of the common steps in early-stage drug discovery is High Throughput Screening (HTS), where up to millions of drug-like compounds are examined against a target protein, and compounds with high affinity are selected. HTS is carried out in a wet lab via experimental methods, where the binding of up to millions of compounds can be examined at significant cost. To increase the efficiency of this process, in silico screening tools emerged in the 1980s 2 called Virtual Screening (VS), which can now evaluate large chemical databases with up to billions of compounds. VS can either rely on the 3D structure information of the target molecules -Structure-Based Virtual Screening (SBVS) -or on known bioactive ligands for the target drug -Ligand-Based Virtual Screening (LBVS). LBVS uses chemical similarity search methods such as QSAR, similarity search, 15 pharmacophore matching or 3D shape matching to find novel ligands that show biological activity with the target of interest. SBVS techniques require the 3D structure of the target protein to be determined by X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, or in silico structure prediction methods. Drug-like molecules are then docked into the binding site of the target protein and scored based on how well they bind to the target using empirical functions. The highest-scoring compounds are selected as hit compounds and passed to the next step for further computational or experimental evaluation. SBVS can use compounds from large databases such as ZINC, ChEMBL, PubChem and NCI, which require heavy computational resources and time. Conventionally, SBVS methods consider a single fixed snapshot of the target protein structure during docking simulation to reduce the computational burden, which can lead to inaccurate predictions of both ligand pose and binding affinity . A variety of methods have been developed over the years to alleviate the deficiencies of singlestructure docking methods. There are more than 60 docking programs and some of them like AutoDock, DOCK, AutoDock Vina, GalaxyDock, and FITTED can incorporate protein flexibility into docking, although this is typically is limited to sidechain motions in the binding pocket. In addition, there are approaches that incorporate protein flexibility using a larger set of protein conformations that are generated either beforehand ("ensemble docking") or on-the-fly ("induced fit docking"). Boltzmann docking and Implicit Ligand Theory are also used to dock ligands to an ensemble of protein conformations, however these ensembles are generated in the apo structure and would not easily capture pocket conformations that are induced by the presence of a ligand. While these algorithms can be accelerated by parallel computing approaches such as message-passing interface (MPI), graphics processing units (GPUs) or MPI-GPU heterogeneous methods, the higher computational expense makes these algorithms unsuitable for virtual screening large ligand databases.