Active perception uses intelligent control strategies applied to the data acquisition process that depend on the current state of data interpretation and has a history that pre-dates computer vision. I will very briefly lay out this history and detail theoretical arguments on the computational nature of the general problem. The theory informs us that optimal solutions are not likely to exist. In this context, I consider the problem of visually finding an object in a mostly unknown space with a mobile robot. It is clear that all possible views and images cannot be examined in a practical system and as a result, this is cast as an optimization problem. The goal is to optimize the probability of finding the target given a fixed cost limit in terms of total number of robotic actions required to find the visual target. Due to the inherent intractability of this problem, we present an approximate solution and investigate its performance and properties.