This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including survey forecasts for predicting euro area inflation and GDP growth at medium-term forecast horizons, using both univariate and multivariate forecasting metrics. Results show that the Survey of Professional Forecasters (SPF) provides good point forecast performance, but scores poorly in terms of densities for all variables and horizons. Accordingly, when individual models are tilted to SPF's first moments and then optimally combined, point accuracy and calibration improve, whereas they worsen when SPF's second moments are included in the tilting. We conclude that judgement incorporated in survey forecasts can considerably increase model forecasts accuracy, however, the way and the extent to which it is incorporated matters.