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Based on the stochastic tunneling method (STUN)  we have developed FlexScreen , a novel strategy for high-throughput in-silico screening of large ligand databases. Each ligand of the database is docked against the receptor using an all-atom representation of both ligand and receptor. The ligands with the best evaluated affinity are selected as lead candidates for drug development. Using the thymidine kinase inhibitors as a prototypical example we documented  the shortcomings of rigid receptor screens in a realistic system. We demonstrate a gain in both overall binding energy and overall rank of the known substrates when two screens with a rigid and flexible (up to 15 sidechain dihedral angles) receptor are compared. We note that the STUN suffers only a comparatively small loss of efficiency when an increasing number of receptor degrees of freedom is considered. FlexScreen thus offers a viable compromise  between docking flexibility and computational efficiency to perform fully automated database screens on hundreds of thousands of ligands. We also investigate enrichment rates  of rigid, soft and flexible receptor models  for 12 diverse receptors using libraries containing up to 13000 molecules. A flexible sidechain model with flexible dihedral angles for up to 12 aminoacids increased both binding propensity and enrichment rates: EF_1 values increased by 35% on average with respect to rigid-docking (3-8 flexible sidechains). This methodology will be soon available for the Cell processor and Pipeline Pilot.