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(eng): To perform efficient inference in Bayesian networks by means of a Junction Tree method, the network graph needs to be triangulated. The quality of this triangulation largely determines the efficiency of the subsequent inference, but the triangulation problem is unfortunately NP-hard. It is common for existing methods to use the treewidth criterion for optimality of a triangulation. However, this criterion may lead to a somewhat harder inference problem than the total table size criterion. We therefore investigate new methods for depth-first search and best-first search for finding optimal total table size triangulations. The search methods are made faster by efficient dynamic maintenance of the cliques of a graph. This problem was investigated by Stix, and in this paper we derive a new simple method based on the Bron-Kerbosch algorithm that compares favourably to Stix' approach. The new approach is generic in the sense that it can be used with other algorithms than just Bron-Kerbosch.