Computational techniques offer an attractive way to understand and predict the products of complex diversity-generating reactions such as the FR . These computational models produce what we have termed complex reaction network representations (CRNRs ), which can help explain the compositions of complex reaction network products. The products of these models can be vetted via their correspondence with experimental data . Though various analytical techniques have tended to focus on biologically relevant compounds, it is clear the molecular diversity generated by the FR is much higher than typically reported, and the reaction likely proceeds in subtly or significantly different ways under different reaction conditions due to variations in the relative rates of the many underlying reaction mechanisms that comprise it.