Traditional environmental and structural monitoring often uses static sensor networks deployed at predetermined locations or mobile robots that use a rastering technique for area coverage. These methods rely on the operators making assumptions about the nature of the unknown field that is being measured and are often time-consuming for localizing an area of interest. Here, we aim to quickly localize possible leaks within high-level nuclear waste tanks at the Hanford facility. The structure of these tanks precludes most sensor network approaches and raises many issues with robotic inspection, such as navigation within highly constrained environments. This work uses a Bayesian Optimization approach for guiding a mobile robot's search strategy and implements a utility function that allows for prior knowledge of the structure to be incorporated when selecting future search locations. Compared to traditional exhaustive approaches, our method quickly reduces RMSE error and shortens the distance the robot must travel.