Monitoring user-system performance in interactive search is a challenging task. Traditional measures of retrieval evaluation, based on recall and precision, are not of any use in real time, for they require a priori knowledge of relevant documents. This paper shows how a Shannon entropy-based measure of user-system performance naturally falls in the framework of (interactive) probabilistic information retrieval. The value of entropy of the distribution of probability of relevance associated with the documents in the collection can be used to monitor search progress in live testing, to allow for example the system to select an optimal combination of search strategies. User profiling and tuning parameters of retrieval systems are other important applications.