Ranked shot lists from 39 automated LSCOM-Lite concept classifiers are investigated with respect to 24 TRECVID 2006 topics. Selecting the best fitting concept or pair of concepts produces the shot set with greatest utility, rather than drawing fewer shots from a larger set of concepts. Mean average precision measures show concept-based shot sets have great utility for topics when perfectly traversed by a user. Using empirical data, however, shows that realistic ability to separate relevant shots from irrelevant ones and recall all the relevant ones is topic-dependent and far from perfect. Concept-based strategies including user-driven selection strategies not using idealized oracle prioritization are also discussed, with implications for query-by-concept in interactive video retrieval as concept spaces grow from tens to thousands.