We explored three ways of dealing with this challenge. First, we leveraged data augmentation at training time to teach the model how to complete a given molecule fragment. Analytical chemists are able to identify which parts of the impurity are identical to the main compound and providing this fragment to the model resulted in significant gains in accuracy. Second, we finetuned the model on simulated MS/MS spectra of structurally related molecules and showed that this approach significantly improved elucidation accuracy. Third, as obtaining such datasets for finetuning is extremely timeconsuming, we attempted to simulate the synthesis process of an asset in silico to predict the impurities that are likely to be generated. We showed that finetuning only on these simulated impurities resulted in a model that is slightly more accurate than one finetuned on historical impurities only, thus enabling elucidation of impurities without the necessity of any prior experimental measurement.