When new experimental setups are being implemented, prediction models and feature sets will need to be updated and developed. For example, while the initial Prosit fragment ion intensity prediction model was trained on data from Thermo Scientific Orbitrap instruments , it was recently optimized to accurately predict fragment ion intensities for timsTOF data as well by finetuning the original model . Similarly, as new instrument platforms are introduced, such as electron activated dissociation provided by Sciex ZenoTOF instruments or data from Thermo Scientific Astral instruments, new prediction models need to be developed. A challenge with developing prediction models for new experimental settings is that initially only a limited amount of training data might be available. Therefore, if there is still some similarity between the new experimental set-up and previous models, such as similar fragmentation methods on related instrument platforms, finetuning can be used instead . In contrast, when the two settings are sufficiently different, retraining new prediction models from scratch might be necessary. When using PSM rescoring there needs to be a careful consideration of the settings in which the machine learning models were developed, as using suboptimal prediction models can lead to unexpected gaps in performance, as demonstrated by the RT example previously described.