An isotopic profile matching algorithm, the isotopic profile deconvoluted chromatogram (IPDC), was developed to screen for a wide variety of organic compounds in high-resolution mass spectrometry (HRMS) data acquired from instruments with resolution power as low as 22 000 fwhm. The algorithm initiates the screening process by generating a series of C/Br/Cl/S isotopic patterns consistent with the profiles of approximately 3 million molecular formulas for compounds with potentially persistent, bioaccumulative, and toxic (PBT) properties. To evaluate this algorithm, HRMS data were screened using these seed profiles to isolate relevant chlorinated and/or brominated compounds. Data reduction techniques included mass defect filtering and retention time prediction from estimated boiling points predicted using molecular formulas and reasonable elemental conformations. A machine learning classifier was also developed using spectrometric and chromatographic variables to minimize false positives. A scoring system was developed to rank candidate molecular formulas for an isotopic feature. The IPDC algorithm was applied to a Lake Michigan lake trout extract analyzed by atmospheric pressure gas chromatography-quadrupole time-of-flight (APGC-QToF) mass spectrometry in positive and negative modes. The IPDC algorithm detected isotopic features associated with legacy contaminants and a series of unknown halogenated features. The IPDC algorithm resolved 313 and 855 halogenated features in positive and negative modes, respectively, in Lake Michigan lake trout.