word2vec is useful when one wants to build a machine learning based system. It allows you to get away with a really small matrix [number of documents,~25-1000]. This really makes ML feasible. Another advantage is preserving context. A vector for car and vehicle are closely aligned.

Problem when implementing a vector based search engine system is that your recall is going to be really high. You will potentially get a lot of marginally related results with your query.

My recommendation will be to implement a tf idf based system. You could enhance your queries by also enriching them with synonyms as well. You could find synonyms by using something like LDA, get a topic model and use the words add the words from that topic in the query.