<p>Identifying contaminants in complex chemical mixtures requires powerful non-targeted analysis methods and tools, especially when it comes to detecting novel transformation products (TPs) and congeners from mixtures. In this study, we provided a comparison of weak versus strong ion exchange solid-phase extraction (SPE) for non-targeted accurate mass profiling of contaminants from wastewater samples and evaluated two data mining tools—Molecular networking and Compound Class Scoring (CCS)—for identification and annotation of transformation products and congeners across multiple municipal wastewater treatment plants. Weak ion exchange resins paired with hydrophilic-lipophilic balance resins showed superior performance in terms of higher average recoveries of spiked internal standards and provided enhanced sensitivity for key contaminant classes including surfactants, pharmaceuticals, and recreational drugs. This was reflected by both greater peak areas and the number of compound identifications. Using these techniques, &gt;800 contaminants were identified at various levels of confidence. Consumer product additives and pharmaceuticals accounted for most of the contaminants in terms of number of identified compounds and abundance. Over 50 TPs of pharmaceuticals and multiple subfamilies of consumer product additive congeners were identified with molecular networking. Polyoxyethylene congeners—namely alkyphenol ethoxylates and linear alkyl ethoxylates—were found in high quantities using CCS, which uses both data-dependent and data-independent acquisition tandem mass spectrometry (MS²) data. The tool showed strong proficiency for large-scale identification of polyoxyethylene congeners, as over 2000 unique compounds were detected. Despite the abundant MS² information, only 9% of these compounds were identified, which underscores the challenge posed by complex industrial mixtures.</p> Graphical abstract <p></p>