For matching our library against the three most commonly used public spectral databases, we used LC-MS/MS spectra from the MassBank of North America (MoNA, , .json), spectra from the MassBanEU database, specifically MassBank_NIST.msp (), and spectra from the GNPS library, namely ALL_GNPS_NO_PROPOGATED (). Prior to matching our MS n Lib's feature list against these public spectral databases (see the first step), the original annotations were removed to keep only spectral library matches. We used the following settings: a minimum matched signals setting of 4, a precursor m/z tolerance and spectral m/z tolerance of 0.005 or 10 ppm, removing the precursor, a weighted cosine similarity with a minimum similarity of 0.6, and the weighting of the square root of the signal intensity (m/z 0 × I 0.5 ). The top five and top ten matches were exported for further evaluation. Here, the feature ID produced by mzmine was used for comparing the annotations by spectral matching with the original compound information. The structures of the matched compounds were cleaned with the same script as in the metadata cleanup and a new InChIKey string was computed. This InChIKey string was used to find spectra that were matched to the identical compound. Finally, to further evaluate the top ten matching hits, we calculated their Tanimoto similarity, based on Morgan fingerprints (radius=2, nBits=2048), and determined their maximum common edge subgraph (MCES), using the default settings within the Python package. The FBMN visualization is provided in Supplementary File 7.