In this paper, we address both standard and focused retrieval tasks based on comprehensible language models and interactive query expansion (IQE). Query topics are expanded using an initial set of Multi Word Terms (MWTs) selected from top n ranked documents. MWTs are special text units that represent domain concepts and objects. As such, they can better represent query topics than ordinary phrases or n-grams. We tested different query representations: bag-of-words, phrases, flat list of MWTs, subsets of MWTs. We also combined the initial set of MWTs obtained in an IQE process with automatic query expansion (AQE) using language models and smoothing mechanism. We chose as baseline the Indri IR engine based on the language model using Dirichlet smoothing. The experiment is carried out on two benchmarks: TREC Enterprise track (TRECent) 2007 and 2008 collections; INEX 2008 Ad-hoc track using the Wikipedia collection.