A major difficulty in designing an automatic explosive hazard detection (EHD) system in forward looking (FL) imagery is the robust and efficient detection of regions of interest (ROIs) that warrant further investigation. FL-EHD is particularly challenging, versus a downward looking technology, because a camera sees everything in the scene, on- and off-road. While off-road can be somewhat mitigated through various mechanisms, such as road masks or a road detector, on-road obstacles still have to be addressed. A brute force strategy is infeasible for this application as it requires advanced standoff capabilities, a low false alarm rate, and real-time processing to achieve a goal such as route clearance or target avoidance. Herein, we discuss the design of a new pre-screener based on shearlet filtering and image post-processing that lets us exploit important characteristics of targets in FL imagery identified by a maximally stable extremal region (MSER) keypoint detector. Results indicate that this approach performs as desired, i.e., identifies expected percentage of target ROIs at the defined acceptable FAR, without need for extensive parameter learning. Performance is assessed in the context of receiver operating characteristic curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day.