The comparative evaluation between SIRIUS and Spec2Mol was performed on the cases where SIRIUS failed to find the exact molecular structure. Since Spec2Mol is intended for recommending potential molecular structures given mass spectra, our intention here is to evaluate how relevant are the recommendations, when compared to widely accepted and state-of-the-art method like SIRIUS. By focusing our comparison on the cases where SIRIUS did not find an exact match, we are essentially evaluating the relevance of the recommended structures when an exact match is not found, which points to the case of novel molecules. In particular, we compared SIRIUS and Spec2Mol on the 307 cases, for which SIRIUS failed to find an exact match, using the metrics based on fingerprint similarity and MCS. It should be noted here that failure to identify the exact structure includes cases where SIRIUS either did not return any structure and cases where the reference structure was not among the predicted structures. The results are summarized in Table . According to our analysis, the structures recommended by Spec2Mol are at least as relevant as the ones recommended by SIRIUS. More specifically, Spec2Mol achieved better cosine similarity for the closest structure, while almost all metrics based on the MCS are improved in the case of Spec2Mol. This outcome is especially interesting and encouraging, given that Spec2Mol is an end-to-end approach that does not take into account any prior knowledge. Spec2Mol generates potential molecular structures by solely looking at raw MS/MS spectra. On the other hand, the combination of CSI:FingerID and SIRIUS attempts to retrieve the exact molecular structure from a reference database taking as input the computed fragmentation tree on top of the raw mass spectra. Although a direct comparison of the two methods is not possible, still the outcome of our comparative evaluation demonstrates that the molecular structures generated by Spec2Mol are at least as successful as the ones obtained by state-of-the-art tools when considering novel molecules despite the fact that Spec2Mol relies solely on raw MS/MS spectra. Elucidating the structure of chemical compounds is a fundamental, but cumbersome, task, in metabolomics studies, as well as in chemical analysis in various domains including drug development and forensics analysis. The available computational tools for aiding structure elucidation are based on fragment annotation and database retrieval methods. This approach fails to identify molecules that are not present in the reference database which, in practice, may correspond to a considerably large percentage of the query spectra. We have developed Spec2Mol, an end-to-end deep learning architecture for directly generating molecular structures (SMILES sequences) from the input MS/MS spectra. Spec2Mol is based on an encoder-decoder architecture that generates molecular SMILES sequences, given mass spectra. While the proposed architecture supports the retrieval of molecules from a database that best matches the input spectra, it can also generate new molecules that have not been seen before in any dataset. Our analysis demonstrates that the recommended molecules are structurally and physiochemically similar to the reference compounds, suggesting that the latent embeddings has indeed learnt informative associations between the spectra and the structural features. When compared to an existing method that depends on the fragmentation tree annotation on top of the raw spectra for molecule identification, Spec2Mol performed on par for the task of recommending potential molecular structures.