Large-scale search engines are built upon huge infrastructures involvingthousands of computers in order to achieve fast response times. In contrast, the energy consumed (and hence the financial cost) is also high, leading to environmental damage. This paper proposes new approaches to increase energy and financial savings in large-scale search engines, while maintaining good query response times. We aim to improve current state-of-the-art models used for balancing power and latency, by integratingnew advanced features. On one hand, we propose to improve the power savings by completely powering down the query servers that are not necessary when the load ofthe system is low. Besides, we consider energy rates into the model formulation. On the other hand, we focus on how to accurately estimate the latency of the whole systemby means of Queueing Theory. Experiments using actual query logs attest the high energy (and financial) savingsregarding current baselines. To the best of our knowledge, this is the first paper in successfully applying stationary Queueing Theory models to estimate the latency in alarge-scale search engine.