We prove a large deviation principle for the greedy exploration of configuration models, building on a time-discretized version of the method proposed by [2] and [4] for jointly constructing a random graph from a given degree sequence and its exploration. The proof of this result follows the general strategy to study large deviations of processes proposed by [9], based on the convergence of non-linear semigroups. We provide an intuitive interpretation of the LD cost function using Crámer’s theorem for the average of random variables with appropriate distribution, depending on the degree distribution of explored nodes. The rate function can be expressed in a closed-form formula, and the large deviations trajectories can be obtained through explicit associated optimization problems. We then deduce large deviations results for the size of the independent set constructed by the algorithm. As a particular case, we analyze these results for d-regular graphs.