Importantly, our prediction is not one structure but a ranked list of proposals. In this ranked list, the structures are sorted by the similarity between our aligned molecular and spectral encodings. An important aspect of the ranked list our system outputs is that it contains both compounds that are part of the grounding database as well as compounds that were not in there but that the genetic algorithm proposed. The retrieved compounds provide relevant context, and since the distribution of those compounds can easily be modified by changing the database (i.e., without retraining the models), it is straightforward to optimize the performance of spec2struct on in-house datasets.