Background: Blood based colorectal cancer (CRC) screening has been validated for use in average risk populations (ECLIPSE NCT#04136002; Guardant Health, USA). Here we present the performance of an enhanced version of a blood-based screening test developed to optimize detection of low shedding tumors by leveraging epigenomic features of cell-free DNA (cfDNA). Test performance was assessed across screen-relevant individuals from the intended use population, including a collection of screen-detected CRC and enriched cohort of individuals with endoscopy finding of CRC. Methods: We trained a regression model to classify whether aberrant cfDNA originated from individuals with CRC or non-ACN individuals and compared performance to an original model in a fixed set of screen-relevant samples sequenced to a median of 11M reads per sample across a panel approximately 1Mb in size. Model optimization focused on identifying training settings and samples to maximize detection of low shedding screen-relevant tumors and introduced noise reduction techniques to minimize technical variation. The final enhanced model was trained in over 4500 samples; the calling cut-off was set targeting 90% specificity in the average-risk population following US-Census age distribution. Analytical Limit of Detection (LoD) was estimated using ~5,000 in silico dilution samples generated from blood samples from 25 CRCs and ~1,650 non-ACNs. Results: This training approach yielded a 2X improvement in the analytical LoD95 (0.004% vs 0.008%) compared to the original model validated in the ECLIPSE trial. The model also yielded an increase in overall CRC clinical sensitivity from 84% to 91%, N=45, with notable sensitivity improvement from 76% to 88% in detecting early localized stage I/II CRC, N = 25, while maintaining specificity at 91%. Conclusions: This enhanced cfDNA-based CRC screening blood test shows improved performance in early stage CRC detection demonstrating the potential of continuous improvement in the performance of cfDNA-based screening tests powered by data and clinical insights.