In protein-ligand docking, the scoring function is responsible for identifying the correct pose of a particular ligand as well as separating ligands from nonligands. Recently there has been considerable interest in schemes that combine results from several scoring functions in an effort to achieve improved performance in virtual screens. One such scheme is consensus scoring, which involves combining the results from several rescoring experiments. Although there have been a number of studies that have investigated factors affecting success in consensus scoring, these studies have not addressed the question of why a rescoring strategy works in the first place. Here we propose and test two alternative hypotheses for why rescoring has the potential to improve results, using GOLD 4.0. The "consensus" hypothesis is that rescoring is a way of combining results from two scoring functions such that only true positives are likely to score highly. The "complementary" hypothesis is that the two scoring functions used in rescoring have complementary strengths; one is better at ranking actives with respect to inactives while the other is better at ranking poses of actives. We find that in general it is this hypothesis that explains success in a rescoring experiment. We also test an assumption of any rescoring method, which is that the scores obtained are representative of the fitness of the docked pose. We find that although rescored poses tended to have slightly higher clash values than their docked equivalents, in general the scores were representative.