We have investigated two major issues in Distributed Information Retrieval published works on these two issues are based on pre-stored metadata, the approaches described in this talk involve extracting the required information at the time the query is processed. In order to predict the relevance of collections to a given query, we analyse a limited number of full documents (e.g., the top five documents) retrieved from each collection and then consider term proximity within them. On the other hand, our merging technique is rather simple since input only requires document scores and lengths of results lists. Our experiments evaluate the retrieval effectiveness of these approaches and compare them with centralised indexing and various other DIR techniques (e.g., CORI). Our evaluations demonstrate that the retrieval effectiveness of our simple approaches is worth considering.