Our approach is not free from limitations. First, we did not make use of any ranking method for the predictions. Several methods for this have been presented in the literature, including for example the modified Platt score based on the predicted CSI:FingerID fingerprints of the input MS/MS spectrum, a dedicated reranking model, ranking based on the predicted spectra of the predicted molecules, or simply ranking by perplexity. All these methods are compatible and directly applicable to our model as well. Second, our finetuning protocol introduced some overfitting to the dataset used for finetuning. The danger of reduced accuracy could be identified at inference time by comparing the input MS/MS spectrum with the finetuning dataset for example via CSI:FingerID fingerprints, and the pretrained model could be used as a fallback. Other finetuning protocols that reduce the risk of overfitting could also be employed. Third, our method is limited to completing fragments with a single extension point. It is not uncommon however for impurities to differ from the main compound in two or more locations, rendering our model unable to directly deal with such cases.