PSM rescoring has emerged as a promising solution to challenges in immunopeptidomics data analysis, reducing false positives and improving identification rates. This typically involves using (semi-)supervised machine learning (ML) algorithms, such as Percolator , to generate a new score, incorporating additional PSM features, to better distinguish between correct and incorrect PSMs, compared to the search engine score. Recent advancements in peptidoform property prediction have led to the incorporation of features for PSM rescoring based on machine learning predictions. For example, fragment ion intensity predictions can now be used as a realistic proxy for ground truth MS/MS data from which spectral similarity features can be calculated for PSM rescoring. In this review, we will explore the tools currently available for PSM rescoring and discuss how they can be used to obtain more information from immunopeptidomics data.