We present a receding-horizon cooperative search algorithm that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target. By sampling the region of interest at locations with high target probability, we reduce the continuous search problem to an optimization on a finite graph. Paths are computed on this graph using a receding horizon approach, in which the horizon is a fixed number of waypoints. To facilitate a fair comparison between paths of varying length on a non-uniform graph, we use an optimization criterion corresponding to the probability of finding the target per unit time. Using this algorithm, we show that the team discovers the target in finite time with probability one. Simulations verify that this algorithm makes effective use of agents and performs significantly better than previously proposed search algorithms. We have also successfully tested this search algorithm on a physical system consisting of two UAVs with gimbal-mounted cameras.