Only 2% of the MS/MS spectra in the training dataset were collected experimentally, with the rest being predicted in silico. Considering the promising model performance, we wanted to quantify how well information from simulated data is transferable to experimental spectra. To evaluate this, we collected all impurities from the internal company database, which amounted to 22,353 molecules and originated 109,343 simulated MS/MS spectra, and we finetuned the model on this dataset after removing all impurities that were already in the test set. We also created a second model that was finetuned on the simulated spectra of the test impurities, in addition to the historical impurities. We then evaluated both models on the experimental spectra of the test impurities (Figure ). The performance of the latter model indicates to what extent it is possible to generalize from simulated to experimental MS/MS spectra of the same molecule, while the former model allows us to evaluate the model's ability to generalize from structurally related molecules, for example by recognizing common structural motifs. While there was no difference in performance when completing fragments missing up to five atoms, the positive effect of finetuning is apparent starting from ten missing atoms (Figure ). Between ten and twenty missing atoms, the pretrained model obtained an accuracy of only 18.5%, while the model finetuned on historical impurities excluding the test ones obtained 68.4% correct predictions. Finetuning on simulated spectra of the test impurities further raised accuracy to 91.1%. The gap between pretrained and finetuned models further widens for de novo elucidation, where the accuracy of 5.2% of the pretrained model was improved to 58.9% by using historical data and 90% when using simulated spectra of test impurities. Our finetuning protocol caused, however, detrimental effects on the performance on molecules that were not related to the finetuning set. In the CASMI challenges, for example, accuracies decreased by 32, 31 and 22 percentage points for the years 2016, 2017 and 2022 respectively. Supplementary Information S4 shows the performance on the CASMI challenges when finetuning the model on their simulated spectra.