Feature-Based Molecular Networking (FBMN) is a method for analyzing non-targeted mass spectrometry data within the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN integrates chromatographic feature detection and alignment tools, and incorporates MS1 information such as isotope patterns and retention time, as well as ion mobility separation when performed. This method enables the distinction of isomers with similar MS2 spectra, facilitates spectral annotation, and incorporates relative quantitative information for robust downstream metabolomics analysis. FBMN is a complementary tool to classical molecular networking (classical MN), which is based on the MS-Cluster algorithm. FBMN uses LC-MS feature abundance (peak area or height) to estimate relative ion intensity, providing more accurate results. FBMN is already the second most utilized analysis tool within the GNPS environment, with over 6,767 jobs performed in 2019. FBMN has been used in more than 80 publications since its development in November 2017. FBMN enables efficient visualization and annotation of isomers in LC-MS2 datasets, as demonstrated with data from a drug discovery project on Euphorbia plant extract and the detection of human microbiome-derived lipids from the American Gut Project. FBMN resolves positional isomers/stereoisomers in molecular networks that have similar MS2 spectra but distinct retention times, which classical MN cannot resolve. FBMN also reduces spectral redundancy and deobfuscates spectral similarity relationships, as shown in the case of EDTA. FBMN enables the use of relative quantification in molecular networks, as demonstrated with the NIST1950 serum reference standard. FBMN enables molecular networking with ion mobility spectrometry, as shown with the NIST 1950 serum analysis. FBMN is integrated with other computational mass spectrometry annotation tools such as SIRIUS, DEREPLICATOR, NAP, MS2LDA, MolNetEnhancer, and Qemistree. FBMN is available as a web-interface on the GNPS web platform and is open-source. FBMN is suitable for advanced molecular networking analysis, enabling the characterization of isomers, the incorporation of relative quantification, and the integration of ion mobility data. FBMN is recommended for analyzing a single LC-MS2 metabolomics study, but its applicability is limited when applied across multiple studies due to different experimental conditions and possible batch effects. FBMN is also limited by the scalability of most feature detection and alignment software tools when analyzing very large datasets. FBMN offers improvements in many aspects of molecular networking analysis, but classical MN remains essential for repository-scale meta-analysis of large datasets. FBMN is available for processing data from various mass spectrometry software, including MZmine, OpenMS, XCMS, MS-DIAL, and MetaboScape. FBMFeature-Based Molecular Networking (FBMN) is a method for analyzing non-targeted mass spectrometry data within the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN integrates chromatographic feature detection and alignment tools, and incorporates MS1 information such as isotope patterns and retention time, as well as ion mobility separation when performed. This method enables the distinction of isomers with similar MS2 spectra, facilitates spectral annotation, and incorporates relative quantitative information for robust downstream metabolomics analysis. FBMN is a complementary tool to classical molecular networking (classical MN), which is based on the MS-Cluster algorithm. FBMN uses LC-MS feature abundance (peak area or height) to estimate relative ion intensity, providing more accurate results. FBMN is already the second most utilized analysis tool within the GNPS environment, with over 6,767 jobs performed in 2019. FBMN has been used in more than 80 publications since its development in November 2017. FBMN enables efficient visualization and annotation of isomers in LC-MS2 datasets, as demonstrated with data from a drug discovery project on Euphorbia plant extract and the detection of human microbiome-derived lipids from the American Gut Project. FBMN resolves positional isomers/stereoisomers in molecular networks that have similar MS2 spectra but distinct retention times, which classical MN cannot resolve. FBMN also reduces spectral redundancy and deobfuscates spectral similarity relationships, as shown in the case of EDTA. FBMN enables the use of relative quantification in molecular networks, as demonstrated with the NIST1950 serum reference standard. FBMN enables molecular networking with ion mobility spectrometry, as shown with the NIST 1950 serum analysis. FBMN is integrated with other computational mass spectrometry annotation tools such as SIRIUS, DEREPLICATOR, NAP, MS2LDA, MolNetEnhancer, and Qemistree. FBMN is available as a web-interface on the GNPS web platform and is open-source. FBMN is suitable for advanced molecular networking analysis, enabling the characterization of isomers, the incorporation of relative quantification, and the integration of ion mobility data. FBMN is recommended for analyzing a single LC-MS2 metabolomics study, but its applicability is limited when applied across multiple studies due to different experimental conditions and possible batch effects. FBMN is also limited by the scalability of most feature detection and alignment software tools when analyzing very large datasets. FBMN offers improvements in many aspects of molecular networking analysis, but classical MN remains essential for repository-scale meta-analysis of large datasets. FBMN is available for processing data from various mass spectrometry software, including MZmine, OpenMS, XCMS, MS-DIAL, and MetaboScape. FBM