We are aware of two studies that have evaluated de novo sequencing tools on external data. First, Beslic et al. compared the performance of Novor, pNovo3, DeepNovo, SMSNet, PointNovo, and Casanovo on the task of de novo sequencing for antibody discovery. To avoid biases due to different training datasets used, they first retrained all six tools on the MassIVE-KB human spectral library . Evaluating the tools on human and mouse antibody data, the authors concluded that Casanovo and PointNovo show improved peptide recall across different enzymes and datasets compared with competing methods. Second, Tran et al. have evaluated PEAKS, PointNovo, Casanovo, and GraphNovo on five datasets: human tryptic data, human non-tryptic data, Arabidopsis thaliana data, HLA class I data, and a simulated dataset generated using Prosit . In contrast to the previous benchmarking effort, the models were not consistently retrained but rather used directly. Because all tools have originally been trained on human data, they achieved the strongest performance on the human test data as well. When evaluating on the A. thaliana data, however, the performances dropped substantially, suggesting some lack of generalizability to data that are dissimilar from the training data. Overall, Casanovo and GraphNovo achieved the best peptide-level performance across all evaluation datasets.