The inverse RNA folding problem deals with designing a sequence of nucleotides that will fold into a desired target structure. Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. It learns a playout policy to intensify the search of the state space near the current best sequence. The algorithm uses a prior on the possible actions so as to perform non uniform playouts when learning the instance of problem at hand. We trained a transformer neural network on the inverse RNA folding problem using the Rfam database. This network is used to generate a prior for every Eterna100 puzzle. GNRPA is used with this prior to solve some of the instances of the Eterna100 dataset. The transformer prior gives better result than handcrafted heuristics.