  Search engine configuration can be quite difficult for inexpert developers.
Instead, an auto-configuration approach can be used to speed up development
time. Yet, such an automatic process usually requires relevance labels to train
a supervised model. In this work, we suggest a simple solution based on query
performance prediction that requires no relevance labels but only a sample of
queries in a given domain. Using two example usecases we demonstrate the merits
of our solution.
