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Purpose: Whole-heart MRA techniques typically target pre-determined motion states and address cardiac and respiratory dynamics independently. We propose a novel fast reconstruction algorithm, applicable to ungated free-running sequences, that leverages inherent similarities in the acquired data to avoid such physiological constraints. Theory and Methods: The proposed SIMilarity-Based Angiography (SIMBA) method clusters the continuously acquired k-space data in order to find a motion-consistent subset that can be reconstructed into a motion-suppressed whole-heart MRA. Free-running 3D radial datasets from six ferumoxytol-enhanced scans of pediatric cardiac patients and twelve non-contrast scans of healthy volunteers were reconstructed with a non-motion-suppressed regridding of all the acquired data (All Data), our proposed SIMBA method, and a previously published free-running framework (FRF) that uses cardiac and respiratory self-gating and compressed sensing. Images were compared for blood-myocardium interface sharpness, contrast ratio, and visibility of coronary artery ostia. Results: Both the fast SIMBA reconstruction (~20s) and the FRF provided significantly higher blood-myocardium sharpness than All Data (P<0.001). No significant difference was observed among the former two. Significantly higher blood-myocardium contrast ratio was obtained with SIMBA compared to All Data and FRF (P<0.01). More coronary ostia could be visualized with both SIMBA and FRF than with All Data (All Data: 4/36, SIMBA: 30/36, FRF: 33/36, both P<0.001) but no significant difference was found between the first two. Conclusion: The combination of free-running sequences and the fast SIMBA reconstruction, which operates without a priori assumptions related to physiological motion, forms a simple workflow for obtaining whole-heart MRA with sharp anatomical structures.