Motivation: Hidden Markov Models (HMMs) have proved to be a powerful tool for protein domain identification in newly sequenced organisms. However, numerous domains may be missed in highly divergent proteins. This is the case for Plasmodium falciparum proteins, the main causal agent of human malaria. Results: We propose a method to improve the sensitivity of HMM domain detection by exploiting the tendency of the domains to appear preferentially with a few other favorite domains in a protein. When sequence information alone is not sufficient to warrant the presence of a particular domain, our method enables its detection on the basis of the presence of other Pfam or InterPro domains. Moreover, a shuffling procedure allows us to estimate the false discovery rate associated with the results. Applied to P. falciparum, our method identifies 585 new Pfam domains (versus the 3683 already known domains in the Pfam database) with an estimated error rate below 20%. These new domains provide 387 new Gene Ontology annotations to the P. falciparum proteome. Analogous and congruent results are obtained when applying the method to related Plasmodium species, P. vivax and P. yoelii. Availability: Supplementary Material and a database of the new domains and GO predictions achieved on Plasmodium proteins are available at http://www.lirmm.fr/~terrapon/codd/