In this paper we present an extension to our QoS optimization algorithm, Q-RAM[7] [11], that can improve optimization time by several orders of magnitude when managing highly configurable tasks. A highly configurable task is one with a large number of QoS dimensions and/or a large number of quality levels on those dimensions. For example, an application that has ten QoS dimensions with ten quality levels each will have 10^10 setpoints, or ways in which it can be configured. While the existing Q-RAM algorithm has been shown to be a very effective resource management tool, it must still explicitly perform computations on all of the setpoints for each task. For tasks with 10^10 setpoints or more, this is clearly impractical. The key idea presented here is a new approximation algorithm for the concave majorant step in Q-RAM. By using this algorithm in a filtering step, the best performing subset of the setpoints can be quickly found without explicitly examining all of the setpoints. The idea is validated using a phased array radar system as an example application.