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Query processing in search engines can be optimized for use for all queries. For this, system component parameters such as the weighting function or the automatic query expansion model can be optimized or learned from past queries. However, it may be more interesting to optimize the processing thread on a query-by-query basis by adjusting the component parameters; this is what selective query processing does. Selective query processing uses one of the candidate processing threads chosen at query time. The choice is based on query features. In this paper, we examine selective query processing in different settings, both in terms of effectiveness and efficiency; this includes selective query expansion and other forms of selective query processing (e.g., when the term weighting function varies or when the expansion model varies). We found that the best trade-off between effectiveness and efficiency is obtained when using the best trained processing thread and its query expansion counter part. This seems to be also the most natural for a real-word engine since the two threads use the same core engine (e.g., same term weighting function).