The Food and Drug Administration recommends prognostic enrichment of randomized controlled trials (RCTs), aimed at restricting the study population to participants most likely to have events and therefore derive benefit from a given intervention. The coronary artery calcium (CAC) score is powerful discriminator of cardiovascular risk, and in this review we discuss how CAC may be used to augment widely used prognostic enrichment paradigms of RCTs of add-on therapies in primary prevention. We describe recent studies in this space, with special attention to the ability of CAC to further stratify risk among guideline-recommended candidates for add-on risk-reduction therapies. Given the potential benefits in terms of sample size, cost reduction, and overall RCT feasibility of a CAC-based enrichment strategy, we discuss approaches that may help maximize its advantages while minimizing logistical barriers and other challenges. Specifically, use of already existing CAC data to avoid the need to re-scan participants with previously documented high CAC scores, use of increasingly available, large clinical CAC databases to facilitate the identification of potential RCT participants, and implementation of machine learning approaches to measure CAC in existing computed tomography images performed for other purposes, will most likely boost the implementation of a CAC-based enrichment paradigm in future RCTs.