The problem of finding remote homologues of a given protein sequence via alignment methods is not fully solved. In fact, the task seems to become more difficult with more data. As the size of the database increases, so does the noise level, the highest alignment scores due to random similarities increase and can be higher than the alignment score between true homologues. Comparing two sequences with an arbitrary alignment method yields a similarity value which may indicate an evolutionary relationship between them. A threshold value is usually chosen to distinguish between true homologue relationships and random similarities. To compensate for the higher probability of spurious hits in larger databases, this threshold is increased. Increasing specificity however leads to decreased sensitivity as a matter of principle. Sensitivity can be recovered by utilizing refined protocols. A number of approaches to this challenge have made use of the fact that proteins are often members of some larger protein family. This can be exploited by using position-specific substitution matrices or profiles, or by making use of transitivity of homology. Transitivity refers to the concept of concluding homology between proteins A and C based on homology between A and a third protein B and between B and C. It has been demonstrated that transitivity can lead to substantial improvement in recognition of remote homologues particularly in cases where the alignment score of A and C is below the noise level. A natural limit to the use of transitivity is imposed by domains. Domains, compact independent sub-units of proteins, are often shared between otherwise distinct proteins, and can cause substantial problems by incorrectly linking otherwise unrelated proteins.