The growing field of urinary proteomics has provided a promising opportunity to identify biomarkers useful for diagnosis and prognostication of a number of diseases. Urine is abundant and readily collected in a non-invasive manner which makes it eminently available for testing, however sample preparation for proteomics analysis remains one of the biggest challenges in this field. As newer technologies to analyze tandem mass spectrometry (MS) data develop, utility of urinary proteomics would be enhanced by better sample processing and separation workflows to generate fast, reproducible, and more in-depth proteomics data. In this study, we have evaluated the performance of four sample preparation methods: MStern, PreOmics In-StageTip (iST), Suspension-trapping (S-Trap), and conventional urea In-Solution trypsin hydrolysis for non-depleted urine samples. Data Dependent Acquisition (DDA) mode on QE-HF was used for single-shot label-free data acquisition. Our results demonstrate a high degree of reproducibility within each workflow. PreOmics iST yields the best digestion efficiency with lowest percentage of missed-cleavage peptides. S-trap workflow gave the greatest number of peptide and protein identifications. Using S-trap method, with 0.5mL urine sample as starting material, we identify ~1500 protein groups and ~17700 peptides from DDA analysis with a single injection. The continued refinement of sample preparation (presented here), LC separation methods and mass spectrometry data acquisition can allow the integration of information rich urine proteomic records across large cohorts with other ‘big data’ initiatives becoming more popular in medical research.