A search engine generally applies a single search strategy to any user query. The search combines many component processes (e.g., indexing, query expansion, search-weighting model, document ranking) and their hyperparameters, whose values are optimized based on past queries and then applied to all future queries. Even an optimized system may perform poorly on some queries, however, whereas another system might perform better on those queries. Selective search strategy aims to select the most appropriate combination of components and hyperparameter values to apply for each individual query. The number of candidate combinations is huge. To adapt best to any query, the ideal system would use many combinations. In the real world it would be too costly to use and maintain thousands of configurations. A trade-off must therefore be found between performance and cost. In this paper, we describe a risksensitive approach to optimize the set of configurations that should be included in a selective search strategy. This approach solves the problem of which and how many configurations to include in the system. We show that the use of 20 configurations results in significantly greater effectiveness than current approaches when tested on three TREC reference collections, by about 23% when compared to L2R documents and about 10% when compared to other selective approaches, and that it offers an appropriate trade-off between system complexity and system effectiveness. CCS CONCEPTS • Information systems → Retrieval models and ranking; Retrieval effectiveness; Information retrieval query processing.