Metabolomics is a valuable tool for discovering metabolic signals for which the compound identification procedure is underprovided and tedious [157]. Advances in bioinformatics have dramatically improved metabolomic **data** processing [158,159], but, despite this, thousands of signals remain unidentified [160]. Thus, metabolomics could have a much greater impact, with the development of improved annotation workflows and more comprehensive plant metabolite libraries. Machine learning now plays a significant role in metabolomics (LC-MS being routinely utilized [161]). It predicts a vast array of molecular properties and processes, such as adduct species, MS fragmentation, MS/MS deconvolution, and chromatographic retention time [158,159,162]. However, it must still be noted that, due to the vast numbers of closely related isomeric compounds in plants, as well as their derivatives decorated with varying isomeric sugar moieties or fused with amino acids, which produce near-identical MS/MS and MS n fragmentation patterns, and which co-elute even with long (60 > min) UHPLC separations, there needs to be a focus upon developing higher resolution separation methodologies and technologies that can be combined with both MS and 2D NMR. The development of 2D LC separations, as well as time-series retention index standards that are applicable to LC, as n-alkanes and fatty acid methyl esters (FAMES) are applied in GC, would provide a massive step change towards developing retention time (RT)-MS/MS and MS n libraries that are transferable between different LC-MS systems and laboratories. Overcoming such hurdles and enhancing our metabolite annotation capabilities will be particularly valuable to the discovery of antimicrobial and viral molecules in plant-pathogen research. A recent study reports the metabolic setting of plant-virus interactions in a natural habitat and its predictive link to susceptibility, unravelling new insight into plant-virus interactions and providing attractive antimicrobial targets [163]. Likewise, machine learning-based predictive metabolomics is particularly promising, to reveal and validate metabolic predictors under biotic stress [164]. Future bioinformatics developments for metabolomics will reduce human intervention and increase **data** analysis throughput, thanks to robotized assays, for instance [157,164].