developed the stochastic surface walking with neural network (SSW-NN) algorithm to speed up the reaction space exploration. The SSW-NN was recently applied to glucose pyrolysis by combining and analyzing multiple SSW trajectories (this study is later compared with this work). In the past year, several additional algorithms have been reported with applications for individual multi-step reactions, and catalysis at surfaces. Despite the large number of reaction steps that can be discovered by some of these algorithms, sampling biases and limited transferability are common problems. For example, the AINR requires reaction conditions that are likely to over-sample high barrier reactions and endothermic products compared with algorithms that use minimum energy pathway searches. Conversely, methods that rely on system-specific heuristics or ML approximations are not transferable to new systems and require extensive customization to apply. It seems accurate to summarize that there are currently no general purpose network exploration policies that have been demonstrated to be accurate when compared against experimental observations of deep reaction networks (e.g., capable of finding plausible low-barrier pathways to all major experimental products, or predicting the relative mass flux of various products based on pathways). This gap motivated the current effort to revisit the problem of glucose pyrolysis using a simple and generic exploration policy based on network theory.