Given a data matrix, unsupervised column subset selection refers to the problem of identifying a subset of columns that can be used to linearly approximate the original data matrix. This problem has many applications, such as feature selection and representative selection, but solving it optimally is known to be NP-hard. We consider multi-view unsupervised column subset selection, which extends the concept of (single-view) column subset selection to data represented in multiple views or modalities. We introduce a combinatorial search algorithm for this generalized problem. One variant of the algorithm is guaranteed to compute an optimal solution in a setting similar to the classical A* algorithm. Other suboptimal variants, in a setting similar to the weighted A* algorithm, are much faster and provide a solution along with a bound on its quality.