We designed and developed a novel framework DWIRS for Deep Exploration with indexing and ranking. The purpose of DWIRS framework is to explore high quality contents from the quantitative Deep Web, with least possible effort, in the least possible time, irrespective of architectural dependencies and operating system related issues. It is imperative to perform indexing and ranking is the nucleus of the exploration process. The design of the proposed framework is carried out in three phases: Indexing, Standard Search and Ranking Search. In phase 1, the acquired Deep Web data is pre-processed through Tokenization, Stop Word Removal and Stemming. Then, indexing is performed for the Deep Web data. The exploration process is carried out based on the search keyword provided by the user. Cosine similarity measure is used in standard search. The search results are re-ranked by using BRF algorithm. The performance of DWIRS framework has been evaluated by developing a customized application on the collected Deep Web data sets. By comparing the results of standard search with that of ranking search, it is shown that retrieval effectiveness as well as the quality of search results has been improvised. Our experimental results revealed that the proposed framework works effectually.