Suspect screening is a valuable tool for characterizing per- and polyfluoroalkyl substances (PFASs) in environmental media. Although a variety of data mining tools have been developed and applied for suspect screening of PFAS, few suspect screening workflows have undergone a comprehensive performance evaluation or optimization. The goals of this research were to: (1) evaluate and optimize three independent suspect screening workflows for the detection of PFASs in water samples; and (2) apply the optimized suspect screening workflows to an environmental sample to determine the extent to which suspect screening results converge. We evaluated and optimized suspect screening workflows using Compound Discoverer v3.2, enviMass v4.2, and FluoroMatch v2.4 using test samples containing 33 target PFASs. The average sensitivity (Sen) and selectivity (Sel) for each workflow across the test samples was: Compound Discoverer Sen = 71%, Sel = 85%; enviMass Sen = 89%, Sel = 80%; FluoroMatch Sen = 51%, Sel = 82%. We then applied the optimized workflows to a contaminated groundwater sample containing an unknown number of PFASs. Each workflow managed to annotate unique PFASs that were not annotated by the other workflows including 2 by Compound Discoverer and 19 each by enviMass and FluoroMatch. Thirty-two enviMass hits and 28 of the Compound Discoverer and FluoroMatch hits were annotated by at least one of the other workflows. Sixteen PFASs were annotated by all three of the optimized workflows. This work provides a basis for conducting suspect screening for PFASs that will lead to more consistent reporting of suspect screening data.