The last event concerns the increasing number of phytochemists who recently have actively engaged in addressing the principles of open science and promoting the production of FAIR data. Significant initiatives have been launched in this context, enabling our research activities to benefit from essential open-access tools in spectral data processing and databases. Although the potential for NPs to reach high added value sectors and the interest in studying them for fundamental research questions are significant, the phytochemistry work often remains complex, time-consuming, tedious, and costly. Together with the entry into force of the Nagoya Protocol, this is one of the reasons for which industrial research programs on natural products have slowed down or even stopped since the beginning of the 2000s. Nevertheless, the massive arrival over the last 15 years of new dereplication approaches enabling known compounds to be chemically characterised as far upstream as possible in the process has given new impetus to phytochemical research, both at academic and industrial levels.(2) Nevertheless, identifying unambiguous structures by describing their unequivocal molecular topology and their geometry, among mixtures, remains challenging. Hence, "annotation" is commonly used when structural information is incomplete. The two main spectroscopic methods used to study complex mixtures of NPs are nuclear magnetic resonance (NMR) and mass spectrometry (MS), often hyphenated with HPLC for the latter.(3) Their sensitivity and universality performance affects the number and the relevance of annotated compounds. As stated by Sumner et al., exploiting the advantages of at least two unrelated techniques (e.g., NMR and MS) should improve the annotation reliability.(4) Various strategies (online versus offline combinations) were reported by and Marshall et al. and are shown in Fig. . When examined under the same conditions, a compound originating from two distinct biological sources is expected to display identical spectral features. Consequently, rapidly identifying well-known compounds, achieved by matching experimental data with established reference spectra, is generally straightforward, assuming the necessary spectral data is accessible. However, this process becomes considerably more intricate when dealing with complex mixtures. Presently, while structures within natural product structural databases are relatively easy to retrieve (e.g., LOTUS, COCO-NUT-DB, NP-MRD, DNP, NPAtlas, ChEBI, KNApSAcK, etc.), the main challenge lies in locating databases where each structure is associated with its corresponding spectral data (e.g., GNPS, MarinLit). Access to comprehensive and easily searchable experimental spectral data remains crucial to ensure a high confidence level in annotations. Nevertheless, natural product databases encounter limitations, such as incompleteness, inconsistency, availability loss, and/or restricted query flexibility. Additionally, data from NMR and MS are scattered throughout scientific publications in formats that do not facilitate automated data extraction for constructing databases. As a result, even when such data exist in the literature, they are not uniformly incorporated into databases. Due to these limitations, querying a database for known natural products is hindered by barriers that significantly slow down the overall data mining process. As a solution, predicting spectral data has emerged as a way to improve current spectral databases. In this context, existing spectral databases serve a broader purpose than expediting dereplication (i.e., identifying known compounds). They also serve as the cornerstone for developing algorithms capable of predicting spectral data (e.g., MetFrag, CFM-ID, QCxMS). These tools can then enrich current experimental spectral databases by incorporating simulated in silico spectral data (e.g., UNPD-ISDB, FooDB, AntiBase, Mona, etc.), subsequently streamlining the dereplication process (e.g., Network Annotation propagation) or aiding to develop robust systems for automatic de novo elucidation (e.g., SIRIUS, Sherlock (7)). However, the DB size and content impact annotation performance, as a more extensive database increases the probability of returning irrelevant candidates, whereas a database that is too restricted a priori may lead to the omission of annotations.( ) DB content should be a priori restricted by taxonomy, either on biological and/or chemical grounds, to reduce the number of irrelevant matching spectra. The build of such DB is nowadays an obstacle course. Large chemical libraries must be first surveyed for compounds, according to criteria defined by the purposes of the study. The molecular structures of the returned compounds must then be gathered into a structural DB. This implies all the issues concerning the stereochemistry description and the drawing of the molecular structures. Finally, these structural DBs should be able to be used as inputs for spectral prediction software. Not all software is user-friendly, and the general workflow, including all the required unit steps, is not automated and available under a single software package.