There is a growing interest in using computational methods to screen for peptides that can be used as targets for cancer immunotherapy, and structure-based methods are particularly interesting for personalized predictions. However, computationally docking peptides to protein receptors remains an open challenge. Part of the issue lies in accurately scoring the binding modes of a protein-peptide complex produced by a molecular docking tool. In this study, we evaluate several popular scoring functions in their ability to accurately rank the best protein-peptide complex conformations, based on the RMSD to the original crystal structure. Our evaluation of scoring shows that existing scoring functions, though known to be adept at scoring drug-like ligands, are limited when it comes to peptides. Therefore, accurately scoring conformations from peptide docking should currently be regarded as the biggest unmet challenge in molecular docking and as an important goal for computational oncology.