This paper describes the development and evaluation of a novel method for estimating parameters in structural econometric models using pretrained neural networks. The core idea is to pre-train an estimator using a large number of datasets simulated from a specific structural model, enabling rapid and accurate estimation on new datasets with minimal computational cost and researcher effort. The authors demonstrate this approach with a sequential search model, showcasing that the pretrained estimator achieves comparable or superior accuracy to traditional methods like Simulated Maximum Likelihood Estimation (SMLE) while being significantly faster. The research highlights the potential for off-the-shelf estimators to increase the accessibility of structural models and facilitate their integration into real-time algorithms.