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Fictitious play is a popular game-theoretic model of learning in games. However, it has received little attention in practical applications to large problems. This paper introduces two variants of fictitious play that are implemented in behavioural strategies of an extensive-form game. The first variant is a full-width process that is realization equivalent to its normal-form counterpart and therefore inherits its convergence guarantees. However, its computational requirements are linear in time and space rather than exponential. The second variant, Fictitious Self-Play, is a machine learning framework that implements fictitious play in a sample-based fashion. Experiments in imperfect-information poker games compare our approaches and demonstrate their convergence to approximate Nash equilibria.