A structural-bioinformatics-based computational methodology has been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from updated canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011), performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. We computationally benchmarked RAbD on a set of ten diverse antibodyantigen complexes, using two novel metrics, the design risk ratio and the antigen risk ratio, for measuring success in computational protein design. These metrics provide a measure of statistical significance typically absent in protein design benchmarking. Risk ratios for all CDRs were above 1.0 for cluster recovery in the presence of the antigen when compared against simulations in the absence of the antigen. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.