The computational costs of the transformer-based multi-vector retrieval model ColBERT depend on the number of vectors used to represent queries and documents. Common strategies to lower the costs thus prune the vectors to a fixed number or relative to the sequence length. We compare standard pruning approaches like weighted token pruning or IDF-based pruning and analyze the impact on the downstream effectiveness of respective ColBERT models. Our experiments indicate that weighted pruning can yield a better effectiveness--efficiency trade-off than other pruning techniques, but we also find that very simplistic pruning techniques can yield very effective ColBERT models when trained properly.