Lung malignancies are among the leading causes of cancer-related death in the modern world. While the disease is relatively manageable if caught at an early stage, the genomic variability and high level of mutations represent a daunting challenge for diagnosis, staging and prognostic classification. One of the hallmarks of lung cancer is dysregulated activity of serine hydrolases, nevertheless, detection of their activity is challenging because bio specimens are available mostly as OCT-embedded tissues and current limitations of analytical workflows. To address the need for a robust and sensitive activity-based proteomic workflow, we established an ABPP-SWATH/DIA-MS platform and applied it for depletion-dependent differential activity profiling of serine hydrolases on a cohort of lung cancer patient samples. We demonstrated it is compatible with sensitive activity-based studies from OCT-embedded tissues by monitoring over 170 hydrolases across the patient sample cohort identifying over 20 dysregulated hydrolases like PREP, DPPIV and ELANE that have elevated activity in tumours. We confirmed our findings with a fluorescent activity-based probe and activity assays to show that our platform can guide the selection of hydrolases for validation in biochemical assays. Importantly, the method could be applied to reinvestigate the existing collections of lung cancer samples for dysregulated hydrolases and thus improve understanding of molecular biology of cancer and select biomarkers for better disease diagnosis and staging