In the context of handwriting recognition, word confusion networks (WCN) are convenient representations of alternative recognition candidates. They provide alignment for mutually exclusive words along with the posterior probability of each word. In this paper, we present a method for indexing on-line handwriting based on WCN. The proposed method exploits the information provided by WCN in order to enhance relevant keyword extraction. In addition, querying the index for a given keyword has worst case complexity O(log n), as compared to usual keyword spotting algorithms which run in O(n). Experiments show promising results in keyword retrieval effectiveness by using WCN when compared to keyword search over 1-best recognition results.