In AC, the rough energy landscape of the target protein is replaced by a smooth attractive energy landscape during sampling. The sampling space is generated by virtual attracting and electrostatic cloud points placed in concave regions of the protein surface. A parameter N T hr allows to tune the placement of attractive points only in deep binding clefts or also in shallower regions. The algorithm generates initial ligand conformations by placing, rotating and minimizing the ligand inside this mold of the protein surface. The sampling procedure can be enhanced by applying small random variations to the initial conditions and using multiple random initial conditions (RIC). The sampling stage is followed by a pose refinement stage in which the protein is reintroduced. Geometry optimizations are first performed with a softcore potential, followed by the standard potential. Finally, the refined poses are scored by the CHARMM force field terms and the Fast Analytical Treatment of Solvation (FACTS) model. To mimic the biological binding process of a covalent ligand (Equation ) we implemented a two-step covalent docking method (Figure ) in AC. Sampling is performed with the noncovalent pre-reactive topology of the ligand (first stage of Equation ), while pose-refinement and scoring is done with its post-reactive topology, which includes the covalent bond with the protein. We call this method the switch method (Figure ) and compare its performance to results obtained with purely non-covalent docking (non-cov, Figure ) and with purely covalent docking, starting directly from the post-reative topology of the ligand (cov-only, Figure ). In the switch method, poses obtained from non-covalent sampling can be filtered by the distance between the reactive sites of the ligand and the protein, to account for the fact that covalent bond formation is unlikely to occur when the reactive sites are far from each other.