Modern experiments in high-energy physics require an increasing amount of simulated data. Monte-Carlo simulation of calorimeter responses is by far the most computationally expensive part of such simulations. Recent works have shown that the application of generative neural networks to this task can significantly speed up the simulations while maintaining an appropriate degree of accuracy. This paper explores different approaches to designing and training generative neural networks for simulation of the electromagnetic calorimeter response in the LHCb experiment.