Let $\psi(u,t)$ be the probability that the workload in an initially empty M/G/1 queue exceeds $u$ at time $t<\infty$, or, equivalently, the ruin probability in the classical Crámer-Lundberg model. Assuming service times/claim sizes to be subexponential, various Monte Carlo estimators for $\psi(u,t)$ are suggested. A key idea behind the estimators is conditional Monte Carlo. Variance estimates are derived in the regularly varying case, the efficiencies are compared numerically and also one of the estimators is shown to have bounded relative error. In part, also extensions to general Léevy processes are treated.