This work explores probabilistic deep learning models as offshore farm-wide virtual load sensors, including Bayesian neural networks, Monte Carlo dropout, and deep neural network ensembles. The aim is to develop models offering uncertainty-aware predictions of damage equivalent loads using SCADA and accelerometer data. This study uses the data from a Belgian offshore wind farm’s five fleet-leader turbines. After training the neural networks with one year of collected data, these models are deployed to another year, facing out-of-distribution data due to changes in operational conditions. The analysis assesses generalization and uncertainty quantification abilities, providing insights into their strengths and weaknesses. Ultimately, our work supports the industrial adoption of probabilistic deep learning virtual monitoring models, enabling informed asset management decisions with predictions and uncertainty measures.