The article introduces Feature-Based Molecular Networking (FBMN), an advanced analysis method within the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN builds on chromatographic feature detection and alignment tools to enhance molecular networking by incorporating isotope patterns, retention times, and ion mobility separation data. This method improves the visualization and annotation of isomers in non-targeted mass spectrometry data, facilitating the identification and isolation of structurally related compounds. FBMN is particularly useful for resolving positional isomers and stereoisomers that produce similar MS² spectra but have distinct retention times. It also enables relative quantification, which is crucial for robust downstream metabolomics statistical analysis. The method is integrated into the GNPS web platform, offering a streamlined workflow for data processing and analysis. The article provides detailed documentation and tutorials for using FBMN with various mass spectrometry data processing tools, including MZmine, OpenMS, XCMS, MS-DIAL, MetaboScape, and Progenesis QI. FBMN has been applied to various datasets, demonstrating its effectiveness in resolving isomers, reducing spectral redundancy, and improving the accuracy of relative quantification. The method is recommended for advanced molecular networking analysis, particularly in the context of LC-MS² metabolomics studies.The article introduces Feature-Based Molecular Networking (FBMN), an advanced analysis method within the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN builds on chromatographic feature detection and alignment tools to enhance molecular networking by incorporating isotope patterns, retention times, and ion mobility separation data. This method improves the visualization and annotation of isomers in non-targeted mass spectrometry data, facilitating the identification and isolation of structurally related compounds. FBMN is particularly useful for resolving positional isomers and stereoisomers that produce similar MS² spectra but have distinct retention times. It also enables relative quantification, which is crucial for robust downstream metabolomics statistical analysis. The method is integrated into the GNPS web platform, offering a streamlined workflow for data processing and analysis. The article provides detailed documentation and tutorials for using FBMN with various mass spectrometry data processing tools, including MZmine, OpenMS, XCMS, MS-DIAL, MetaboScape, and Progenesis QI. FBMN has been applied to various datasets, demonstrating its effectiveness in resolving isomers, reducing spectral redundancy, and improving the accuracy of relative quantification. The method is recommended for advanced molecular networking analysis, particularly in the context of LC-MS² metabolomics studies.