Peptide mass fingerprinting has evolved to become a staple piece in the proteomics toolbox. In fact, PMF was the kickstarter for more recent and more scalable bottom-up techniques, such as shotgun proteomics. Since its introduction as a simple tool to identify proteins from their unique digested peptide fingerprint by means of comparison of the obtained masses to a database, the computational possibilities have made an unthinkable leap forward. While the traditional ways of PMF are trodden by countless researchers around the world, one can easily imagine the sheer endless possibilities to supercharge the capabilities of the PMF workflow using machine learning in both the upstream and downstream domains. Modern clustering algorithms have the unique potential to identify complex motifs in peptide mass fingerprints and dramatically enhance the current identification possibilities. Becoming aware of this potential can be reminiscent of the step that criminalistics took when capturing the physical fingerprint of a perpetrator was complemented by the recognition of the genetic fingerprint.