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Web search engines often involve a complex pipeline of processing stages including computing, scoring, and ranking potential answers plus returning the sorted results. The latency of such pipelines can be improved by minimizing data movement, making stages faster, and merging stages. The throughput is determined by the stage with the smallest capacity and it can be improved by allocating enough parallel resources to each stage. In this paper we explore the possibility of employing hardware acceleration (an FPGA) as a way to improve the overall performance when computing answers to search queries. With a real use case as a baseline and motivation, we focus on accelerating the scoring function implemented as a decision tree ensemble, a common approach to scoring and classification in search systems. Our solution uses a novel decision tree ensemble implementation on an FPGA to: 1) increase the number of entries that can be scored per unit of time, and 2) provide a compact implementation that can be combined with previous stages. The resulting system, tested in Amazon F1 instances, significantly improves the quality of the search results and improves performance by two orders of magnitude over the existing CPU based solution.