At present, the main instrumental setup used to characterize complex NEs is ultra-high-performance liquid chromatography coupled with data-dependant acquisition (DDA) tandem high-resolution mass spectrometry (UHPLC-HRMS 2 ) . The resulting data is usually processed to obtain a list of LC-MS features -a chromatographic peak with a given m/z, retention time (RT), area/intensity -and their associated MS 2 spectrum. The features from all considered samples are then classically aligned based on their RT and m/z to obtain a feature quantification table of dimensions N x M (N represents the number of samples and M the number of features) and their associated fragmentation spectra. The latter can be used to perform structural annotation, and the quantitative, spectral, and structural data can then be integrated and visualized through feature-based molecular networking (FBMN) . FBMN is a technique used to organize features' fragmentation spectra in spectral similarity clusters for visualization and analysis. In addition to the quantification table, different extracts' metadata, such as taxonomical origin or bioactivity, can be integrated into the FBMN to enhance its analysis and interpretation. Multi-informative FBMN can then help to highlight groups of features specific to active extracts or a given taxon .