A more thorough review of existing methodologies for metabolite identification, including in silico fragmentation tools, fingerprint prediction and fragmentation trees, was recently presented by Nguyen et al. with a focus on machine learning (ML) approaches . It should be noted here that early ML-based approaches were built on shallow ML models, such as Support Vector Machines (SVMs) and Random Forests (RFs), applied either on features extracted from the mass spectra or the fragmentation trees, and also kernel-based methods to judge similarity between either spectra or fragmentation trees. However, lately there is a growing interest in exploring Deep Learning (DL) architectures for the development of computation tools to support structure elucidation. There have been efforts to learn spectra embeddings that can be subsequently used to assess spectral similarity when searching in spectral databases . Additionally, there are DL-based methodologies for clustering spectra, either for identifying the compound class or for medical diagnosis differentiating between healthy and cancerous tissues . Most DL-based methodologies that operate directly on spectra data are based on Convolutional Neural Networks (CNNs) representing the spectrum as a vector that indicates the intensities of each fragment mass . The CNN attempts to automatically identify spectra features replacing the need for manual featurization. Architectures that have adopted concepts from Natural Language Processing (NLP) have also emerged representing the mass spectrum as text and the mass peaks as words . Due to the limited amount of mass spectra data different workarounds have been investigated including hybrid approaches , combining statistical ML models and DL architectures, and approaches based on transfer learning .