This paper presents our work for the Microblog Track in FIRE 2016. The task involved utilizing microblog data (tweets) to retrieve useful information during times of disasters. In particular, given a set of tweets posted during the Nepal earthquake in 2015, the goal was to judge relevance of each tweet against a set of topics which reflected useful information needs. Our approach made use of manual query formation for searching relevant tweets based on the information required for each topic, after indexing them using Lucene.