Background: The characterization of protein-peptide interactions is a challenge for computational molecular docking. Protein-peptide docking tools face at least two major difficulties: (1) efficient sampling of large-scale conformational changes induced by binding and (2) selection of the best models from a large set of predicted structures. In this paper, we merge an efficient sampling technique with external information about side-chain contacts to sample and select the best possible models. Methods: In this paper we test a new protocol that uses information about side-chain contacts in CABS-dock protein-peptide docking. As shown in our recent studies, CABS-dock enables efficient modeling of large-scale conformational changes without knowledge about the binding site. However, the resulting set of binding sites and poses is in many cases highly diverse and difficult to score. Results: As we demonstrate here, information about a single side-chain contact can significantly improve the prediction accuracy. Importantly, the imposed constraints for side-chain contacts are quite soft. Therefore, the developed protocol does not require precise contact information and ensures large-scale peptide flexibility in the broad contact area. Conclusions: The demonstrated protocol provides the extension of the CABS-dock method that can be practically used in the structure prediction of protein-peptide complexes guided by the knowledge of the binding interface.