Here we present a new strategy for designing and filtering potentially massive combinatorial libraries using structural information of a binding site. We have developed a variation of the structural interaction fingerprint (SIFt) named r-SIFt, which incorporates the binding interactions of variable fragments in a combinatorial library. This method takes into account the 3D structure of the active site of the target molecule and translates desirable ligand-target binding interactions into library filtering constraints. We show using the MAP kinase p38 as a test case that we can efficiently analyze and classify compounds on the basis of their abilities to interact with the target in the desired binding mode. On the basis of these classifications, decision tree models were generated using the molecular descriptors of the compounds as predictor variables. Our results suggest that r-SIFt coupled with the classification models should be a valuable tool for structure-based focusing of combinatorial chemical libraries.