In the framework of risk assessment in nuclear accident analysis, best-estimate computer codes are used to estimate safety margins. Several inputs of the code can be uncertain, due to a lack of knowledge but also to the particular choice of accidental scenario being considered. The objective of this work is to identify the most penalizing (or critical) configurations (corresponding to extreme values of the code output) of several input parameters (called "scenario inputs"), independently of the uncertainty of the other input parameters. However, complex computer codes, as the ones used in thermal-hydraulic accident scenario simulations, are often too CPU-time expensive to be directly used to perform these studies. A solution consists in fitting the code output by a metamodel, built from a reduced number of code simulations. When the number of input parameters is very large (around a hundred here), the metamodel building remains a challenge. To overcome this, we propose a methodology, called ICSCREAM (Identification of penalizing Configurations using SCREening And Metamodel), based on screening techniques and Gaussian process (Gp) metamodeling. The Gp metamodel is used to estimate, within a Bayesian framework, the conditional probabilities of exceeding a critical value, according to the scenario inputs. Critical configurations of these inputs are then identified. The efficiency of this methodology is illustrated on a thermal-hydraulic industrial case simulating an accident of primary coolant loss in a Pressurized Water Reactor with 97 uncertain inputs and two scenario inputs to be penalized.