However, pathfinding is useful only if one already knows what molecule to search for. Stochastic sampling with kMC, unlike graph-based pathfinding, enables the exploration of a reactive space without a specific target. This is because, while kMC trajectories can be used to search for a specific species, they are neither produced with any species in mind, nor are they biased towards any species. As a result, a unique capability of our approach is the ability to identify products of a CRN with minimal prior knowledge. To do this, we apply a set of heuristic criteria to the collection of trajectories (Figure ). In line with the commonsense notion of a reaction product, we define a network product as any species that i) is on average formed significantly more than it is consumed; ii) accumulates significantly in the final state of an average trajectory; and iii) can be reached by low-cost reaction pathways (see Methods). We note that the specific products that are identified depend on threshold values for these heuristics, which are arbitrarily selected. We further emphasize that the heuristics just described essentially require the elimination of endergonic reactions and reactive loops, as described above. If a species is involved in one or many loops, then the back-and-forth reactions would make exact counts of formation and consumption reactions meaningless. In addition, with loops present, a kMC simulation can in principle proceed indefinitely, which makes definition of accumulation in a "final" state problematic.