The docking and scoring paradigm can be considered as the combination of two separate problems. The first aspect is a geometric, or more broadly an informatics problem: how can we place a solid object (ligand) within a “cavity“ of another solid (protein) or close to another molecule in a well-defined Cartesian space? The second one is a more intriguing chemical problem: how can we properly predict the free energy of binding considering all the possible contributions involved in biological interactions? There is a wide range of algorithms and approaches used to produce docking poses and, consequently, a wide range of associated scoring functions used to judge the possible poses. In several cases the scoring functions are deeply entwined with the search method and can not be considered separately. In other cases, more than one scoring function is provided in docking programs, each showing different strengths and limitations. Consensus scoring approaches, combining multiple methods into a single metric, have been created to overcome the weaknesses characterizing the different docking algorithms and the associated scoring functions. Correctly predicting not just the binding mode, but also the binding energy, is a primary exigency in all docking simulations and, in particular, in virtual screening applications. Accurate estimation of binding free energy would allow, not only good discrimination between active and inactive molecules, but also among closely related analogs, this latter case being particularly important for drug design. In this chapter we discuss problems related to docking/scoring techniques for in silico screening and we review the most common scoring methods.