For the calculation of conditional expectations in nonlinear filtering of Markov processes, one may think to use Monte Carlo techniques, as an alternative to the numerical solution of Zakai equation (a stochastic PDE). We show that a direct implementation of this idea is unefficient, and we propose a modified algorithm, that uses importance sampling, where our choice of the new probability is based on large deviations arguments.