Catalysis is a graded phenomenon, and its efficacy may be gauged experimentally by the fold enhancement of a reaction's rate compared to its spontaneous rate. Enzyme catalytic power for different enzyme-substrate pairs spans up to 20 orders of magnitude . By the same token, non-enzymatic catalysis, considered relevant to prebiotic chemistry as reviewed , spans several orders of magnitude . In the basic depictions of the CAS model, a binary rule is used (catalyzed or not) with unspecified power of catalysis. In contrast, GARD implements a graded scale of different catalytic capacities, each with an assigned probability (Figure ). Indeed, a key challenge in modeling a graded scale of catalytic power is generating a realistic, nature-like probability distribution for different values of catalytic efficacy. Resting on the concept of shape space and string matching as a means to quantitate molecular recognition , we have developed and experimentally verified (42) a receptor affinity distribution for analyzing the immune and olfactory system. Subsequently, based on the notion that polymer sequence can be mapped to folded structures, which in turn project to catalytic effectiveness , the coveted probability distribution of catalytic power can be derived using models based on string matching. This has been applied to mutually catalytic networks both in the CAS realm and in GARD simulations (Figure ). GARD simulations have also demonstrated that the ensuing lognormal distribution of catalytic power is vital for reproduction dynamics . Observing and reanalyzing published data, a similar distribution has been shown to appear in experiments systematically measuring catalytic power among amphiphiles and other small molecules (Figure ). Such a biology-like distribution of the GARD catalytic network also makes it scalable , as it approximates power-law/scale-free properties.