We present a learning-to-rank approach for resource selection. We develop features for resource ranking and present a training approach that does not require human judgments. Our method is well-suited to environments with a large number of resources such as selective search, is an improvement over the state-of-the-art in resource selection for selective search, and is statistically equivalent to exhaustive search even for recall-oriented metrics such as MAP@1000, an area in which selective search was lacking.