MOTIVATION: Design of protein-protein interaction (PPI) inhibitors is a key challenge in structural bioinformatics and computer-aided drug design. Peptides, which partially mimic the interface area of one of the interacting proteins, are natural candidates to form protein-peptide complexes competing with the original PPI. The prediction of such complexes is especially challenging due to the high flexibility of peptide conformations. RESULTS: In this article, we present PepCrawler, a new tool for deriving binding peptides from protein-protein complexes and prediction of peptide-protein complexes, by performing high-resolution docking refinement and estimation of binding affinity. By using a fast path planning approach, PepCrawler rapidly generates large amounts of flexible peptide conformations, allowing backbone and side chain flexibility. A newly introduced binding energy funnel 'steepness score' was applied for the evaluation of the protein-peptide complexes binding affinity. PepCrawler simulations predicted high binding affinity for native protein-peptide complexes benchmark and low affinity for low-energy decoy complexes. In three cases, where wet lab data are available, the PepCrawler predictions were consistent with the data. Comparing to other state of the art flexible peptide-protein structure prediction algorithms, our algorithm is very fast, and takes only minutes to run on a single PC. AVAILABILITY: http://bioinfo3d.cs.tau.ac.il/PepCrawler/ CONTACT: [email protected]; [email protected].