A major bottleneck in metabolomics is the annotation of a molecular formula as a first step to a tentative structure assignment of known and unknown metabolites. The direct observation of an isotopic fine structure (IFS) provides the ability to confidently assign an unknown's molecular formula out of a complex mass spectrum. However, the majority of mass spectrometers deployed for metabolomic studies do not have sufficient resolving power and high-fidelity isotope ratios in the mass range of interest to determine molecular formulas from IFS data. To increase the number of unknowns for which IFS can be determined, a segmented "boxcar" approach using a selection quadrupole as a broadband mass filter is used. In this longer, enhanced dynamic range discovery experiment, selected ions in a specific mass range are accumulated before detection by the analyzer cell. The mass filter window is then moved across the entire mass range resulting in a composite mass spectrum covering the m/z range of interest for phenomics research. The effectiveness of the FIA-CASI-FTMS workflow utilizing IFS for molecular formula assignment is realized with the implementation of the dynamically harmonized cell, which distinguishes the approach from other segmented workflows because of the analytical properties of the cell. The discovery approach was applied to a human plasma sample to confidently assign an unknown molecular formula as part of the quest to illuminate its metabolic "dark matter" via high-fidelity IFS ratio determinations. The FIA-CASI-FTMS workflow showed a 2.6-fold increase in both matching with the Human Metabolome Database and an increase in the IFS pattern.