The main analytical targets of synthetic cannabinoids are often metabolites. With the high number of new psychoactive substances entering the market, suitable workflows are needed for analytical target identification in biological samples. The aims of this study were to identify the main metabolites of the synthetic cannabinoids, AMB-CHMICA and 5C-AKB48, using an in silico-assisted workflow with analytical data acquired using ultra-high-performance liquid chromatography–(ion mobility spectroscopy)–high resolution–mass spectrometry in data-independent acquisition mode (UHPLC-(IMS)-HR-MSE). The metabolites were identified after incubation with rat and pooled human hepatocytes using UHPLC-HR-MSE, followed by UHPLC-IMS-HR-MSE. Metabolites of AMB-CHMICA and 5C-AKB48 were predicted with Meteor (Lhasa Ltd) and imported to the UNIFI software (Waters). The predicted metabolites were assigned to analytical components supported by the UNIFI in silico fragmentation tool. The main metabolic pathway of AMB-CHMICA was O-demethylation and hydroxylation of the methylhexyl moiety. For 5C-AKB48, the main metabolic pathways were hydroxylation(s) of the adamantyl moiety and oxidative dechlorination with subsequent oxidation to the ω-COOH. The matrix components in the metabolite spectra were reduced with IMS, which improved the accuracy of the spectral interpretation; however, this left fewer fragment ions for assigning sites of metabolism. Meteor was able to predict the majority of the metabolites, with the most notable exception being the oxidative dechlorination and, consequently, all metabolites that underwent that transformation pathway. Oxidative dechlorination of ω-chloroalkanes in humans has not been previously reported in the literature. The postulated metabolites can be used for screening of biological samples, with four-dimensional identification based on retention time, collision cross section, precursor ion, and fragment ions.