In this paper, we compare active learning strategies for indexing concepts in video shots. Active learning is simulated using subsets of a fully annotated dataset instead of actually calling for user intervention. Training is done using the collaborative annotation of 39 concepts of the TRECVID 2005 campaign. Performance is measured on the 20 concepts selected for the TRECVID 2006 concept detection task. The simulation allows exploring the effect of several parameters: the strategy, the annotated fraction of the dataset, the size of the dataset, the number of iterations and the relative difficulty of concepts.